Papers by Siddharth Goyal
“John is 50 years old, can his son be 65?” Evaluating NLP Models’ Understanding of Feasibility (2023.eacl-main)
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Himanshu Gupta, Neeraj Varshney, Swaroop Mishra, Kuntal Kumar Pal, Saurabh Arjun Sawant, Kevin Scaria, Siddharth Goyal, Chitta Baral
| Challenge: | Recent work has found that large-scale language models lack commonsense reasoning ability . a dataset evaluating large-level language models is needed to evaluate their understanding of feasibility . |
| Approach: | They propose a question-answering dataset that tests understanding of feasibility . they propose to use commonsense reasoning to reason about when an action is feasible . |
| Outcome: | The proposed dataset shows that state-of-the-art models struggle to answer feasibility questions correctly. |
InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis (2024.naacl-short)
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| Challenge: | Experimental results on the Sem Eval 2014, 15, and 16 datasets demonstrate that InstructABSA outperforms the previous state-of-the-art (SOTA) approaches on Term Extraction (ATE), Sentiment Classification(ATSC) and Sentimence Pair Extraction(ASPE) subtasks. |
| Approach: | They introduce positive, negative, and neutral examples to each training sample, and instruction tune the model (Tk-Instruct) for ABSA subtasks. |
| Outcome: | The proposed model outperforms the state-of-the-art (SOTA) on Term Extraction (ATE), Sentiment Classification (ATSC) and Sentimence Pair Extractions (ASPE) subtasks. |
Simulating Opinion Dynamics with Networks of LLM-based Agents (2024.findings-naacl)
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Yun-Shiuan Chuang, Agam Goyal, Nikunj Harlalka, Siddharth Suresh, Robert Hawkins, Sijia Yang, Dhavan Shah, Junjie Hu, Timothy Rogers
| Challenge: | Existing approaches to simulating opinion dynamics often over-simplify human behavior . authors propose refining LLMs with real-world discourse to better simulate evolution of beliefs . |
| Approach: | They propose to use large language models to simulate opinion dynamics in groups of simulated agents . they found that LLM agents produce more accurate information than ABMs . |
| Outcome: | The proposed model can be used to better simulate opinion dynamics in real-world discourses. |