Papers by Kushan Mitra
Characterizing Large Language Models as Rationalizers of Knowledge-intensive Tasks (2024.findings-acl)
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| Challenge: | Large language models generate fluent text with minimal task-specific supervision, but their ability to generate rationales for knowledge-intensive tasks (KITs) remains under-explored. |
| Approach: | They propose to generate retrieval-augmented rationalization of KIT model predictions via external knowledge guidance within a few-shot setting. |
| Outcome: | The proposed rationales were compared with crowd-sourced rationale models on factuality, sufficiency, and convincingness. |
RECAP: REwriting Conversations for Intent Understanding in Agentic Planning (2026.findings-eacl)
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| Challenge: | Understanding user intent is essential for effective conversational assistants . however, real-world dialogues are often ambiguous, underspecified, or dynamic . |
| Approach: | They propose a benchmark to evaluate intent rewriting in user-agent dialogues . they propose rewriters that reframe user-goal dialogues into concise representations of user goals . |
| Outcome: | The proposed benchmark outperforms baselines in terms of plan preference and fine-tuning two DPO-based rewriters yields additional utility gains. |
Verification-Aware Planning for Multi-Agent Systems (2026.eacl-long)
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| Challenge: | Large language model (LLM) agents are increasingly deployed to tackle complex tasks . multi-agent collaboration introduces new challenges in planning, coordination, and verification . |
| Approach: | They propose a framework for multi-agent collaboration with verification-aware planning . the framework decomposes tasks, models subtask dependencies, and encodes planner-defined passing criteria as subtask verification functions (VFs) |
| Outcome: | The proposed framework outperforms baselines on diverse datasets while improving system robustness and interpretability. |
AIPOM: Agent-aware Interactive Planning for Multi-Agent Systems (2025.emnlp-demos)
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| Challenge: | Large language models (LLMs) are being used for planning in orchestrated multi-agent systems . existing LLMs fall short of human expectations and lack effective mechanisms for users to inspect, understand, and control their behaviors. |
| Approach: | They propose a system supporting human-in-the-loop planning through conversational and graph-based interfaces. |
| Outcome: | AIPOM enables users to transparently inspect, refine, and collaboratively guide LLM-generated plans, significantly enhancing user control and trust in multi-agent workflows. |
FactLens: Benchmarking Fine-Grained Fact Verification (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have shown impressive capability in language generation and understanding, but their tendency to hallucinate and produce factually incorrect information remains a key limitation. |
| Approach: | They propose a benchmark to evaluate fine-grained fact verification where claims are broken down into smaller sub-claims for individual verification. |
| Outcome: | The proposed model enables more precise identification of inaccuracies, improved transparency, and reduced ambiguity in evidence retrieval. |
MEGAnno+: A Human-LLM Collaborative Annotation System (2024.eacl-demo)
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| Challenge: | Large language models (LLMs) can label data faster and cheaper than humans . however, they may fall short in understanding of complex contexts, leading to incorrect labels . |
| Approach: | They propose a collaborative approach where humans and LLMs work together to produce reliable labels. |
| Outcome: | The proposed system can produce reliable and high-quality labels faster and cheaper than humans . compared to traditional models, it can generate labels faster, at a lower cost . |