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Tracing Undesirable LLM Behaviors

This is an official implementation of Where Did It Go Wrong? Attributing Undesirable LLM Behaviors via Representation Gradient Tracing

Get Started

  1. Run pip install -r requirements.txt
  2. Run python finetune.py --dataset <dataset_name> --model <model_name> to train the model
  3. Run python generate.py --model <model_name> --lora <lora_adapter> --dataset <test_data> to check whether any undesirable behaviors occur on the test set, and then put them in ./datasets/validation/
  4. Run python tracing.py --model <model_name> --lora <lora_adapter> --method <tracing_method> --topk <topk> to trace the detected undesirable behaviors back to the corresponding training samples

For full-funetuning, run python full_funetune.py --dataset <dataset_name> --model <model_name>. After training, modify the model loading logic in generate.py and tracing.py to load your fully fine-tuned model. Refer to these code files for implementation details.

Citation

If you find this repo useful, please cite our paper.

@article{li2025did,
title={Where Did It Go Wrong? Attributing Undesirable LLM Behaviors via Representation Gradient Tracing},
author={Li, Zhe and Zhao, Wei and Li, Yige and Sun, Jun},
journal={arXiv preprint arXiv:2510.02334},
year={2025}
}

Contact

If you have any questions or want to discuss some details, please contact zheli@smu.edu.sg.

Acknowledgement

We appreciate the following github repos a lot for their valuable code base or datasets:

https://github.com/ykwon0407/DataInf

https://github.com/princeton-nlp/LESS

About

Attributing Undesirable LLM Behaviors via Representation Gradient Tracing

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Tracing Undesirable LLM Behaviors
This is an official implementation of Where Did It Go Wrong? Attributing Undesirable LLM Behaviors via Representation Gradient Tracing
Get Started
  1. Run pip install -r requirements.txt
  2. Run python finetune.py --dataset <dataset_name> --model <model_name> to train the model
  3. Run python generate.py --model <model_name> --lora <lora_adapter> --dataset <test_data> to check whether any undesirable behaviors occur on the test set, and then put them in ./datasets/validation/
  4. Run python tracing.py --model <model_name> --lora <lora_adapter> --method <tracing_method> --topk <topk> to trace the detected undesirable behaviors back to the corresponding training samples
For full-funetuning, run python full_funetune.py --dataset <dataset_name> --model <model_name>. After training, modify the model loading logic in generate.py and tracing.py to load your fully fine-tuned model. Refer to these code files for implementation details.
Citation
If you find this repo useful, please cite our paper.
@article{li2025did,
title={Where Did It Go Wrong? Attributing Undesirable LLM Behaviors via Representation Gradient Tracing},
author={Li, Zhe and Zhao, Wei and Li, Yige and Sun, Jun},
journal={arXiv preprint arXiv:2510.02334},
year={2025}
}

Contact
If you have any questions or want to discuss some details, please contact zheli@smu.edu.sg.
Acknowledgement
We appreciate the following github repos a lot for their valuable code base or datasets:
https://github.com/ykwon0407/DataInf
https://github.com/princeton-nlp/LESS
About
Attributing Undesirable LLM Behaviors via Representation Gradient Tracing
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Tracing Undesirable LLM Behaviors
This is an official implementation of Where Did It Go Wrong? Attributing Undesirable LLM Behaviors via Representation Gradient Tracing
Get Started
  1. Run pip install -r requirements.txt
  2. Run python finetune.py --dataset <dataset_name> --model <model_name> to train the model
  3. Run python generate.py --model <model_name> --lora <lora_adapter> --dataset <test_data> to check whether any undesirable behaviors occur on the test set, and then put them in ./datasets/validation/
  4. Run python tracing.py --model <model_name> --lora <lora_adapter> --method <tracing_method> --topk <topk> to trace the detected undesirable behaviors back to the corresponding training samples
For full-funetuning, run python full_funetune.py --dataset <dataset_name> --model <model_name>. After training, modify the model loading logic in generate.py and tracing.py to load your fully fine-tuned model. Refer to these code files for implementation details.
Citation
If you find this repo useful, please cite our paper.
@article{li2025did,
title={Where Did It Go Wrong? Attributing Undesirable LLM Behaviors via Representation Gradient Tracing},
author={Li, Zhe and Zhao, Wei and Li, Yige and Sun, Jun},
journal={arXiv preprint arXiv:2510.02334},
year={2025}
}

Contact
If you have any questions or want to discuss some details, please contact zheli@smu.edu.sg.
Acknowledgement
We appreciate the following github repos a lot for their valuable code base or datasets:
https://github.com/ykwon0407/DataInf
https://github.com/princeton-nlp/LESS
About
Attributing Undesirable LLM Behaviors via Representation Gradient Tracing
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Readme
Activity
Stars
5 stars
Watchers
0 watching
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0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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Tracing Undesirable LLM Behaviors
This is an official implementation of Where Did It Go Wrong? Attributing Undesirable LLM Behaviors via Representation Gradient Tracing
Get Started
  1. Run pip install -r requirements.txt
  2. Run python finetune.py --dataset <dataset_name> --model <model_name> to train the model
  3. Run python generate.py --model <model_name> --lora <lora_adapter> --dataset <test_data> to check whether any undesirable behaviors occur on the test set, and then put them in ./datasets/validation/
  4. Run python tracing.py --model <model_name> --lora <lora_adapter> --method <tracing_method> --topk <topk> to trace the detected undesirable behaviors back to the corresponding training samples
For full-funetuning, run python full_funetune.py --dataset <dataset_name> --model <model_name>. After training, modify the model loading logic in generate.py and tracing.py to load your fully fine-tuned model. Refer to these code files for implementation details.
Citation
If you find this repo useful, please cite our paper.
@article{li2025did,
title={Where Did It Go Wrong? Attributing Undesirable LLM Behaviors via Representation Gradient Tracing},
author={Li, Zhe and Zhao, Wei and Li, Yige and Sun, Jun},
journal={arXiv preprint arXiv:2510.02334},
year={2025}
}

Contact
If you have any questions or want to discuss some details, please contact zheli@smu.edu.sg.
Acknowledgement
We appreciate the following github repos a lot for their valuable code base or datasets:
https://github.com/ykwon0407/DataInf
https://github.com/princeton-nlp/LESS
About
Attributing Undesirable LLM Behaviors via Representation Gradient Tracing
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
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Tracing Undesirable LLM Behaviors
This is an official implementation of Where Did It Go Wrong? Attributing Undesirable LLM Behaviors via Representation Gradient Tracing
Get Started
  1. Run pip install -r requirements.txt
  2. Run python finetune.py --dataset <dataset_name> --model <model_name> to train the model
  3. Run python generate.py --model <model_name> --lora <lora_adapter> --dataset <test_data> to check whether any undesirable behaviors occur on the test set, and then put them in ./datasets/validation/
  4. Run python tracing.py --model <model_name> --lora <lora_adapter> --method <tracing_method> --topk <topk> to trace the detected undesirable behaviors back to the corresponding training samples
For full-funetuning, run python full_funetune.py --dataset <dataset_name> --model <model_name>. After training, modify the model loading logic in generate.py and tracing.py to load your fully fine-tuned model. Refer to these code files for implementation details.
Citation
If you find this repo useful, please cite our paper.
@article{li2025did,
title={Where Did It Go Wrong? Attributing Undesirable LLM Behaviors via Representation Gradient Tracing},
author={Li, Zhe and Zhao, Wei and Li, Yige and Sun, Jun},
journal={arXiv preprint arXiv:2510.02334},
year={2025}
}

Contact
If you have any questions or want to discuss some details, please contact zheli@smu.edu.sg.
Acknowledgement
We appreciate the following github repos a lot for their valuable code base or datasets:
https://github.com/ykwon0407/DataInf
https://github.com/princeton-nlp/LESS
About
Attributing Undesirable LLM Behaviors via Representation Gradient Tracing
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Readme
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Stars
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0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Tracing Undesirable LLM Behaviors
This is an official implementation of Where Did It Go Wrong? Attributing Undesirable LLM Behaviors via Representation Gradient Tracing
Get Started
  1. Run pip install -r requirements.txt
  2. Run python finetune.py --dataset <dataset_name> --model <model_name> to train the model
  3. Run python generate.py --model <model_name> --lora <lora_adapter> --dataset <test_data> to check whether any undesirable behaviors occur on the test set, and then put them in ./datasets/validation/
  4. Run python tracing.py --model <model_name> --lora <lora_adapter> --method <tracing_method> --topk <topk> to trace the detected undesirable behaviors back to the corresponding training samples
For full-funetuning, run python full_funetune.py --dataset <dataset_name> --model <model_name>. After training, modify the model loading logic in generate.py and tracing.py to load your fully fine-tuned model. Refer to these code files for implementation details.
Citation
If you find this repo useful, please cite our paper.
@article{li2025did,
title={Where Did It Go Wrong? Attributing Undesirable LLM Behaviors via Representation Gradient Tracing},
author={Li, Zhe and Zhao, Wei and Li, Yige and Sun, Jun},
journal={arXiv preprint arXiv:2510.02334},
year={2025}
}

Contact
If you have any questions or want to discuss some details, please contact zheli@smu.edu.sg.
Acknowledgement
We appreciate the following github repos a lot for their valuable code base or datasets:
https://github.com/ykwon0407/DataInf
https://github.com/princeton-nlp/LESS
About
Attributing Undesirable LLM Behaviors via Representation Gradient Tracing
Resources
Readme
Activity
Stars
5 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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BranchesTags
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Tracing Undesirable LLM Behaviors
This is an official implementation of Where Did It Go Wrong? Attributing Undesirable LLM Behaviors via Representation Gradient Tracing
Get Started
  1. Run pip install -r requirements.txt
  2. Run python finetune.py --dataset <dataset_name> --model <model_name> to train the model
  3. Run python generate.py --model <model_name> --lora <lora_adapter> --dataset <test_data> to check whether any undesirable behaviors occur on the test set, and then put them in ./datasets/validation/
  4. Run python tracing.py --model <model_name> --lora <lora_adapter> --method <tracing_method> --topk <topk> to trace the detected undesirable behaviors back to the corresponding training samples
For full-funetuning, run python full_funetune.py --dataset <dataset_name> --model <model_name>. After training, modify the model loading logic in generate.py and tracing.py to load your fully fine-tuned model. Refer to these code files for implementation details.
Citation
If you find this repo useful, please cite our paper.
@article{li2025did,
title={Where Did It Go Wrong? Attributing Undesirable LLM Behaviors via Representation Gradient Tracing},
author={Li, Zhe and Zhao, Wei and Li, Yige and Sun, Jun},
journal={arXiv preprint arXiv:2510.02334},
year={2025}
}

Contact
If you have any questions or want to discuss some details, please contact zheli@smu.edu.sg.
Acknowledgement
We appreciate the following github repos a lot for their valuable code base or datasets:
https://github.com/ykwon0407/DataInf
https://github.com/princeton-nlp/LESS
About
Attributing Undesirable LLM Behaviors via Representation Gradient Tracing
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Readme
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0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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Tracing Undesirable LLM Behaviors
This is an official implementation of Where Did It Go Wrong? Attributing Undesirable LLM Behaviors via Representation Gradient Tracing
Get Started
  1. Run pip install -r requirements.txt
  2. Run python finetune.py --dataset <dataset_name> --model <model_name> to train the model
  3. Run python generate.py --model <model_name> --lora <lora_adapter> --dataset <test_data> to check whether any undesirable behaviors occur on the test set, and then put them in ./datasets/validation/
  4. Run python tracing.py --model <model_name> --lora <lora_adapter> --method <tracing_method> --topk <topk> to trace the detected undesirable behaviors back to the corresponding training samples
For full-funetuning, run python full_funetune.py --dataset <dataset_name> --model <model_name>. After training, modify the model loading logic in generate.py and tracing.py to load your fully fine-tuned model. Refer to these code files for implementation details.
Citation
If you find this repo useful, please cite our paper.
@article{li2025did,
title={Where Did It Go Wrong? Attributing Undesirable LLM Behaviors via Representation Gradient Tracing},
author={Li, Zhe and Zhao, Wei and Li, Yige and Sun, Jun},
journal={arXiv preprint arXiv:2510.02334},
year={2025}
}

Contact
If you have any questions or want to discuss some details, please contact zheli@smu.edu.sg.
Acknowledgement
We appreciate the following github repos a lot for their valuable code base or datasets:
https://github.com/ykwon0407/DataInf
https://github.com/princeton-nlp/LESS
About
Attributing Undesirable LLM Behaviors via Representation Gradient Tracing
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