Papers by Jimmy Huang
A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets (2023.findings-acl)
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Md Tahmid Rahman Laskar, M Saiful Bari, Mizanur Rahman, Md Amran Hossen Bhuiyan, Shafiq Joty, Jimmy Huang
| Challenge: | Currently, the evaluation of large language models (LLMs) such as ChatGPT in academic datasets is difficult due to the difficulty of evaluating the generative outputs produced by this model against the ground truth. |
| Approach: | They evaluate ChatGPT across 140 tasks and analyze 255K responses it generates in academic datasets. |
| Outcome: | The proposed model performs well on 140 tasks and generates 255K responses in these datasets. |
Can Large Language Models Fix Data Annotation Errors? An Empirical Study Using Debatepedia for Query-Focused Text Summarization (2023.findings-emnlp)
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| Challenge: | Debatepedia dataset limited by noise and most queries do not have relevance to document . |
| Approach: | They harness the language generation capabilities of two LLMs to regenerate queries in a Debatepedia dataset. |
| Outcome: | The proposed model can regenerate queries from the Debatepedia dataset. |
Judging the Judges: Can Large Vision-Language Models Fairly Evaluate Chart Comprehension and Reasoning? (2025.acl-industry)
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Md Tahmid Rahman Laskar, Mohammed Saidul Islam, Ridwan Mahbub, Ahmed Masry, Mizanur Rahman, Amran Bhuiyan, Mir Tafseer Nayeem, Shafiq Joty, Enamul Hoque, Jimmy Huang
| Challenge: | Large Vision-Language Models (LVLMs) are expensive and time-consuming to evaluate . however, they are limited in their use in industrial settings due to their limited availability and limited resources. |
| Approach: | They evaluate 13 open-source LVLMs as judges for diverse chart comprehension and reasoning tasks. |
| Outcome: | The proposed models can be used to assess chart comprehension and reasoning tasks, but they are expensive and time-consuming. |
A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations (2024.emnlp-main)
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Md Tahmid Rahman Laskar, Sawsan Alqahtani, M Saiful Bari, Mizanur Rahman, Mohammad Abdullah Matin Khan, Haidar Khan, Israt Jahan, Amran Bhuiyan, Chee Wei Tan, Md Rizwan Parvez, Enamul Hoque, Shafiq Joty, Jimmy Huang
| Challenge: | Large Language Models (LLMs) have gained significant attention due to their capabilities in performing diverse tasks across domains. |
| Approach: | They review the primary challenges and limitations causing inconsistencies in evaluations . early models could generate coherent text but limited to simple tasks . |
| Outcome: | The proposed evaluations are reproducible, reliable, and robust. |
How to Enable Effective Cooperation Between Humans and NLP Models: A Survey of Principles, Formalizations, and Beyond (2025.acl-long)
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| Challenge: | Using large language models, intelligent models have evolved into autonomous agents . this paradigm has yielded remarkable progress in numerous NLP tasks in recent years . |
| Approach: | They present a review of human-model cooperation, exploring its principles, formalizations, and open challenges. |
| Outcome: | The proposed model-model cooperation paradigm has been a key focus of recent research . it is a novel paradigm that can be applied to a variety of tasks . |
When Can We Trust LLMs in Mental Health? Large-Scale Benchmarks for Reliable LLM Evaluation (2026.eacl-long)
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Abeer Badawi, Elahe Rahimi, Md Tahmid Rahman Laskar, Sheri Grach, Lindsay Bertrand, Lames Danok, Prathiba Dhanesh, Jimmy Huang, Frank Rudzicz, Elham Dolatabadi
| Challenge: | Existing benchmarks for large language models are limited in scale, authenticity, and reliability due to the emotionally complex nature of therapeutic dialogue. |
| Approach: | They propose two benchmarks that provide a framework for evaluating large language models for mental health support. |
| Outcome: | The proposed framework provides a framework for generation and evaluation of large-scale authentic dialogue datasets and judge-reliability assessments. |
CNNs for NLP in the Browser: Client-Side Deployment and Visualization Opportunities (N18-5)
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| Challenge: | a JavaScript implementation of a convolutional neural network performs feedforward inference completely in the browser. |
| Approach: | They propose a JavaScript implementation that performs feedforward inference completely in the browser. |
| Outcome: | The proposed model performs feedforward inference completely in the browser without server requests . the proposed model is useful for applications with stringent latency requirements or low connectivity . |
Deploying Tiny LVLM Judges for Real-World Evaluation of Chart Models: Lessons Learned and Best Practices (2025.emnlp-industry)
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Md Tahmid Rahman Laskar, Mohammed Saidul Islam, Ridwan Mahbub, Mizanur Rahman, Amran Bhuiyan, Israt Jahan, Mir Tafseer Nayeem, Shafiq Joty, Enamul Hoque, Jimmy Huang
| Challenge: | Large Vision-Language Models (LVLMs) with only 7B parameters perform poorly as judges in resource-constrained settings. |
| Approach: | They propose two approaches to ensure costefficient evaluation by combining multiple criteria into a single query and domainadaptive transfer learning to create a 2Bparameter VLM on a chart dataset. |
| Outcome: | The proposed model can effectively transfer knowledge from one dataset to another to make it a more specialized model. |
Lost in Translation: Do LVLM Judges Generalize Across Languages? (2026.findings-acl)
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Md Tahmid Rahman Laskar, Mohammed Saidul Islam, Mir Tafseer Nayeem, Amran Bhuiyan, Mizanur Rahman, Shafiq Joty, Enamul Hoque, Jimmy Huang
| Challenge: | MM-JudgeBench is the first large-scale benchmark for multilingual and multimodal judge model evaluation. |
| Approach: | They propose a multilingual benchmark for multilingual and multimodal judge model evaluation that includes over 60K pairwise preference instances spanning 25 typologically diverse languages. |
| Outcome: | The proposed benchmark includes over 60K pairwise preference instances spanning 25 languages. |
Improving Automatic Evaluation of Large Language Models (LLMs) in Biomedical Relation Extraction via LLMs-as-the-Judge (2025.acl-long)
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| Challenge: | Large Language Models generate human-like text, making them unreliable for biomedical relation extraction tasks. |
| Approach: | They propose to use Large Language Models as judges to evaluate biomedical relation extraction . they propose structured output formatting for LLM-generated responses that helps LLMs improve their performance by 15%. |
| Outcome: | The proposed method improves LLM-Judges' performance by 15% . it is cheaper and more efficient than human evaluation metrics, the authors say . |
Learning Query Adaptive Anchor Representation for Inductive Relation Prediction (2023.findings-acl)
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| Challenge: | Existing methods to infer the missing links between entities are limited to the transductive setting . Query Adaptive Anchor Representation (QAAR) model is based on entity-independent features . |
| Approach: | They propose a query adaptive anchor representation model which extracts one opening subgraph and performs reasoning by one time for all candidate triples. |
| Outcome: | The proposed model outperforms state-of-the-art models in relation prediction task. |