Papers by Anmol Goel
Responsible Evaluation of AI for Mental Health (2026.acl-long)
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Hiba Arnaout, Anmol Goel, H. Andrew Schwartz, Steffen T. Eberhardt, Dana Atzil-Slonim, Gavin Doherty, Brian Schwartz, Wolfgang Lutz, Tim Althoff, Munmun De Choudhury, Hamidreza Jamalabadi, Raj Sanjay Shah, Flor Miriam Plaza-del-Arco, Dirk Hovy, Maria Liakata, Iryna Gurevych
| Challenge: | Existing approaches to evaluating AI tools in this domain remain fragmented and inconsistent. |
| Approach: | They propose a taxonomy of AI mental health support types that integrates clinical soundness, social context, and equity to provide a structured basis for evaluation. |
| Outcome: | The proposed framework integrates clinical soundness, social context, and equity, providing a structured basis for evaluation. |
X-RiSAWOZ: High-Quality End-to-End Multilingual Dialogue Datasets and Few-shot Agents (2023.findings-acl)
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Mehrad Moradshahi, Tianhao Shen, Kalika Bali, Monojit Choudhury, Gael de Chalendar, Anmol Goel, Sungkyun Kim, Prashant Kodali, Ponnurangam Kumaraguru, Nasredine Semmar, Sina Semnani, Jiwon Seo, Vivek Seshadri, Manish Shrivastava, Michael Sun, Aditya Yadavalli, Chaobin You, Deyi Xiong, Monica Lam
| Challenge: | X-RiSAWOZ dataset has more than 18,000 human-verified dialogue utterances for each language . Xiaoping and Xinhui are the main challenges for task-oriented dialogue research . |
| Approach: | They develop a toolkit to accelerate the post-editing of a new language dataset after translation . their dataset, code, and toolkit are released open-source . |
| Outcome: | The proposed toolkit accelerates the post-editing of a new language dataset after translation. |
SyMCoM - Syntactic Measure of Code Mixing A Study Of English-Hindi Code-Mixing (2022.findings-acl)
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| Challenge: | Recent work on code mixing in computational settings has leveraged social media code mixed texts to train NLP models. |
| Approach: | They propose to use language ID tags to measure syntactic variety in code-mixed text and their relationship with computational model performance. |
| Outcome: | The proposed measure can be applied to English(en)-hindi(hi) code-mixed datasets and compares them with other measures. |
HLDC: Hindi Legal Documents Corpus (2022.findings-acl)
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Arnav Kapoor, Mudit Dhawan, Anmol Goel, Arjun T H, Akshala Bhatnagar, Vibhu Agrawal, Amul Agrawal, Arnab Bhattacharya, Ponnurangam Kumaraguru, Ashutosh Modi
| Challenge: | Existing systems that process legal documents are lacking high-quality corpora in low resource languages such as Hindi. |
| Approach: | They propose a Hindi Legal Documents Corpus (HLDC) that contains 900K legal documents in Hindi. |
| Outcome: | The proposed model is based on a corpus of more than 900K legal documents in Hindi. |
Privacy Collapse: Benign Fine-Tuning Can Break Contextual Privacy in Language Models (2026.acl-long)
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| Challenge: | Fine-tuning of frontier models can lead to privacy collapse, causing optimisation for helpfulness, exposure to user information, and debugging code printing internal variables. |
| Approach: | They propose to fine-tune frontier models to adapt to specific domains and align with organizational workflows and user preferences. |
| Outcome: | The proposed model fails to perform on safety and utility benchmarks while exhibiting severe privacy vulnerabilities. |
Auditing Language Model Unlearning via Information Decomposition (2026.eacl-long)
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| Challenge: | Existing approaches to unlearning in language models do not account for information about forgotten data . despite the apparent success of unlearning, information about the forgotten data remains linearly decodable from internal representations. |
| Approach: | They propose an interpretable framework for auditing unlearning using Partial Information Decomposition . they propose a representation-based risk score that can guide abstention on sensitive inputs . |
| Outcome: | The proposed framework can guide abstention on sensitive inputs at inference time. |
MASEval: Extending Multi-Agent Evaluation from Models to Systems (2026.acl-demo)
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Cornelius Emde, Alexander Rubinstein, Anmol Goel, Ahmed Heakl, Sangdoo Yun, Seong Joon Oh, Martin Gubri
| Challenge: | MASEval provides a framework-agnostic, system-level comparison across any agent framework and benchmark. |
| Approach: | They propose a Python library that treats the entire agentic system as the unit of analysis. |
| Outcome: | The proposed framework treats the entire agentic system as the unit of analysis. |
An Unsupervised, Geometric and Syntax-aware Quantification of Polysemy (2022.emnlp-main)
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| Challenge: | Polysemy is the phenomenon where a single word form possesses two or more related senses. |
| Approach: | They propose an unsupervised framework to quantify polysemy for words in multiple languages . they use syntactic knowledge to infuse the framework with syntaktic knowledge . |
| Outcome: | The proposed framework is based on syntactic knowledge and is compared with existing methods in English, French and Spanish. |