Papers by Ivaxi Sheth
LLM Task Interference: An Initial Study on the Impact of Task-Switch in Conversational History (2024.emnlp-main)
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| Challenge: | Recent advances in Natural Language Processing (NLP) have led to the widespread deployment of large language models (LLMs) across various applications. |
| Approach: | They propose to formalize the study of task-switches in conversational LLMs by analyzing conversational history. |
| Outcome: | The proposed study formalizes and investigates the sensitivity of large language models to taskswitch scenarios in conversational LLMs. |
Context-Aware Reasoning On Parametric Knowledge for Inferring Causal Variables (2025.findings-emnlp)
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| Challenge: | randomized experiments provide strong inferences, but are often infeasible due to ethical or practical constraints. |
| Approach: | They propose a benchmark where the objective is to complete a partial causal graph. |
| Outcome: | The proposed benchmarks show that they can hypothesize backdoor variables between a cause and its effect. |
CausalGraph2LLM: Evaluating LLMs for Causal Queries (2025.findings-naacl)
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| Challenge: | Recent advances in Large Language Models (LLMs) have opened up new avenues for their use beyond standard Natural Language Processing tasks. |
| Approach: | They propose a benchmark to evaluate the capabilities of Large Language Models (LLMs) they use over 700k queries to compare their encoding capabilities. |
| Outcome: | The proposed benchmark compared LLMs on graph-level and node-level queries and open-sourced and closed models. |
Funny or Persuasive, but Not Both: Evaluating Fine-Grained Multi-Concept Control in LLMs (2026.eacl-short)
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| Challenge: | Large Language Models (LLMs) provide strong generative capabilities, but many applications require explicit and fine-grained control over specific textual concepts. |
| Approach: | They propose a framework for fine-grained controllability for single- and dual-concept scenarios . they find performance drops in the dual-constituency setting, even though chosen concepts should be separable . |
| Outcome: | The proposed framework shows that models struggle with compositionality even when concepts are intuitively independent. |
Justice in Judgment: Unveiling (Hidden) Bias in LLM-assisted Peer Reviews (2026.findings-acl)
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| Challenge: | Existing studies show that large language models carry implicit biases across race, gender, and religion . prior studies documented such biase based on text generation and classification tasks . |
| Approach: | They investigate bias in large language models by controlling metadata on author metadata . authors found affiliation bias favoring authors from highly ranked institutions . |
| Outcome: | The proposed model favors authors from highly ranked institutions, the authors show . the model also favors author affiliations from highly-ranked institutions . |