Papers by Md Nayem Uddin
From Recall to Forgetting: Benchmarking Long-Term Memory for Personalized Agents (2026.findings-acl)
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| Challenge: | Existing benchmarks frame long-term memory evaluation as fact retrieval from past conversations, providing limited insight into agents’ ability to consolidate memory over time or handle frequent knowledge updates. |
| Approach: | They propose a long-term memory benchmark that evaluates three memory-grounded tasks: remembering, reasoning, and recommending. |
| Outcome: | The proposed benchmarks evaluate three tasks: remembering, reasoning, and recommending. |
Generating Uncontextualized and Contextualized Questions for Document-Level Event Argument Extraction (2024.naacl-long)
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| Challenge: | Existing methods for document-level argument extraction do not require human involvement and combine uncontextualized and contextualized questions. |
| Approach: | They propose multiple question generation strategies for document-level event argument extraction that do not require human involvement and combine uncontextualized and contextualized questions. |
| Outcome: | The proposed questions do not require human involvement and are suitable for document-level argument extraction. |
Chaos with Keywords: Exposing Large Language Models Sycophancy to Misleading Keywords and Evaluating Defense Strategies (2024.findings-acl)
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| Challenge: | sycophancy is a type of hallucination in Large Language Models, which can lead to false information being presented. |
| Approach: | They explore the sycophantic tendencies of Large Language Models where models provide accurate answers even if they are not entirely correct. |
| Outcome: | The proposed models generate factually correct statements even when they are not completely correct. |
Insights into Alignment: Evaluating DPO and its Variants Across Multiple Tasks (2025.acl-srw)
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| Challenge: | Large Language Models (LLMs) excel in math reasoning problemsolving, text generation, summarization, creative writing, among other tasks. |
| Approach: | They evaluate Direct Preference Optimization and its variants for aligning Large Language Models with human preferences. |
| Outcome: | The proposed alignment methods achieve near-optimal performance even with smaller subsets of training data. |
UnSeenTimeQA: Time-Sensitive Question-Answering Beyond LLMs’ Memorization (2025.acl-long)
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Md Nayem Uddin, Amir Saeidi, Divij Handa, Agastya Seth, Tran Cao Son, Eduardo Blanco, Steven Corman, Chitta Baral
| Challenge: | UnSeenTimeQA is a data contamination-free time-sensitive question-answering benchmark. |
| Approach: | They propose a data contamination-free time-sensitive question-answering benchmark that avoids web-searchable queries grounded in the real world. |
| Outcome: | The proposed benchmark avoids web-searchable queries grounded in the real world and enables on-demand generation of new samples, mitigating the risk of data leakage. |
ThinkTuning: Instilling Cognitive Reflections without Distillation (2025.emnlp-main)
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| Challenge: | Recent advances in test-time scaling have led to the emergence of thinking LLMs that exhibit self-reflective behaviors and multi-step reasoning. |
| Approach: | They propose a GRPO-based interactive training approach that augments the rollouts of a student model with the guidance of . a teacher poses a problem, lets the student try an answer, then gives corrective feedback–enough to point the mind in the right direction and then show the correct solution. |
| Outcome: | The proposed method shows 3.69% improvement over zero-shot baselines and 2.08% and 3.99% improvement over the vanilla-GRPO baselines. |
Asking and Answering Questions to Extract Event-Argument Structures (2024.lrec-main)
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| Challenge: | Traditionally, corpora are limited to arguments within the same sentence, and inter-sentential arguments are more challenging and have received less attention. |
| Approach: | They propose a question-answering approach to extract document-level event-argument structures by automating questions for each argument type an event may have. |
| Outcome: | The proposed model outperforms previous models and is especially beneficial to extract arguments that appear in different sentences than the event trigger. |
BEMEAE: Moving Beyond Exact Span Match for Event Argument Extraction (2025.naacl-long)
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| Challenge: | Event Argument Extraction (EAE) is a complex task that requires deep comprehension of text to accurately identify and classify event arguments. |
| Approach: | They propose a new evaluation metric that integrates deterministic components with a semantic matching component for more accurate assessment. |
| Outcome: | The proposed evaluation metric leads to higher F1 scores and significant changes in model rankings, underscoring ESM’s inadequacy for comprehensive evaluation of EAE. |