Papers by Archiki Prasad
MeetingQA: Extractive Question-Answering on Meeting Transcripts (2023.acl-long)
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| Challenge: | Meeting transcripts are a promising domain for natural language tasks . lack of annotated data impedes research on other important tasks in this domain . |
| Approach: | They propose an extractive QA dataset comprising questions asked by meeting participants and corresponding responses. |
| Outcome: | The proposed dataset extracts questions asked by meeting participants and corresponding responses from transcripts. |
ADaPT: As-Needed Decomposition and Planning with Language Models (2024.findings-naacl)
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Archiki Prasad, Alexander Koller, Mareike Hartmann, Peter Clark, Ashish Sabharwal, Mohit Bansal, Tushar Khot
| Challenge: | Large Language Models (LLMs) are increasingly being used for interactive decision-making tasks requiring planning and adapting to the environment. |
| Approach: | They propose an approach that explicitly plans and decomposes complex sub-tasks when the LLM is unable to execute them. |
| Outcome: | The proposed approach significantly outperforms established strong baselines, achieving success rates up to 28.3% higher in ALFWorld, 27% in WebShop, and 33% in TextCraft. |
Multi-Attribute Steering of Language Models via Targeted Intervention (2025.acl-long)
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| Challenge: | Existing approaches for steering large language models fail to scale to multi-attribute settings with conflicts, such as enhancing helpfulness while also reducing toxicity. |
| Approach: | They propose a steering framework for selective token-level intervention across multiple attributes that enforcing sparsity and orthogonality among vectors for different attributes. |
| Outcome: | The proposed framework outperforms existing ITI and parameter-efficient fine-tuning approaches across question answering tasks and generative tasks. |
AdaCAD: Adaptively Decoding to Balance Conflicts between Contextual and Parametric Knowledge (2025.naacl-long)
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| Challenge: | Existing contrastive methods that ignore the context of a large language model (LLM) fail to handle instances that vary in their amount of conflict, with static methods over-adjusting when conflict is absent. |
| Approach: | They propose a fine-grained, instance-level approach called AdaCAD which dynamically adjusts the degree of conflict based on the degree. |
| Outcome: | The proposed approach outperforms baselines and improves factuality of summaries by 6.19. |
PRInTS: Reward Modeling for Long-Horizon Information Seeking (2026.acl-long)
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| Challenge: | Existing PRMs cannot capture richer dimensions of information-seeking steps, such as tool interactions and reasoning over tool outputs. |
| Approach: | They propose a generative PRM trained with dual capabilities that compresses the growing context while preserving essential information for step evaluation. |
| Outcome: | PRInTS improves on FRAMES, GAIA, and WebWalkerQA models while preserving essential information for step evaluation. |
Soft Self-Consistency Improves Language Models Agents (2024.acl-short)
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| Challenge: | Current “sample and select” methods rely on majority voting to score answers . however, when tasks have many distinct and valid answers, selection by voting requires a large number of samples. |
| Approach: | They introduce a method that replaces SC's discontinuous scoring with a continuous score computed from model likelihoods to increase selection even when actions are sparsely distributed. |
| Outcome: | The proposed method improves performance and efficiency on long-horizon interactive tasks by replacing SC’s discontinuous scoring with a continuous score computed from model likelihoods. |
How Accents Confound: Probing for Accent Information in End-to-End Speech Recognition Systems (2020.acl-main)
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| Challenge: | a new study examines how accent information is encoded and propagated in an end-to-end ASR system. |
| Approach: | They propose to use phone probes to analyze phonetic content of representations at each layer. |
| Outcome: | The proposed model is based on a large amount of US-accented English speech and is compared with other models using phone probes. |
GrAInS: Gradient-based Attribution for Inference-Time Steering of LLMs and VLMs (2026.acl-long)
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| Challenge: | Existing methods for fine-tuning large language models often ignore token-level causal influence and underutilize model logits. |
| Approach: | They propose a novel approach that uses a gradient-based approach to identify influential tokens and construct directional steering vectors based on their contribution to preferred over dispreferred outputs. |
| Outcome: | The proposed approach outperforms fine-tuning and prior steering methods on both LLM and VLM tasks without degrading fluency or general capabilities. |
MAgICoRe: Multi-Agent, Iterative, Coarse-to-Fine Refinement for Reasoning (2025.emnlp-main)
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| Challenge: | Excessive refinement can cause over-correction and reduce performance, authors say . they say MAgICoRe is a framework for multi-agent iteration for coarse-to-fine refinement . |
| Approach: | They propose a framework for multi-agent iteration for coarse-to-fine refinement that reduces excessive refinement by categorizing problems as easy or hard. |
| Outcome: | The proposed framework beats Self-Consistency by 3.4%, Best-of-k by 3.2%, and Self-Refine by 4.0% on Llama-3-8B and GPT- 3.5. |
ReCEval: Evaluating Reasoning Chains via Correctness and Informativeness (2023.emnlp-main)
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| Challenge: | Existing methods focus on whether the reasoning chain leads to the correct conclusion, but this view may confound reasoning quality with other spurious shortcuts to predict the answer. |
| Approach: | They propose a framework that evaluates reasoning chains via two key properties: (1) correctness, i.e., each step makes a valid inference based on information contained within the step, preceding steps, and input context, and (2) informativeness, respectively. |
| Outcome: | The proposed framework evaluates reasoning chains via two key properties: (1) correctness, i.e., each step makes a valid inference based on information contained within the step, preceding steps, and input context, and (2) informativeness, which is helpful towards deriving the generated answer. |
GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models (2023.eacl-main)
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| Challenge: | Recent work aimed to improve task performance of large language models by rewriting or tuning them manually, but manual rewrite is time-consuming and requires subjective interpretation. |
| Approach: | They propose a gradient-free, edit-based search approach for improving task instructions for large language models. |
| Outcome: | The proposed approach outperforms manual rewriting and purely example-based prompts while allowing for API-based tuning. |