Papers by Scott Sanner
Verifiable, Debuggable, and Repairable Commonsense Logical Reasoning via LLM-based Theory Resolution (2024.emnlp-main)
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| Challenge: | Recent advances in Large Language Models (LLMs) have led to substantial interest in their application to commonsense reasoning tasks. |
| Approach: | They propose a logical reasoning framework that integrates commonsense knowledge with a verifiable logical framework that mitigates hallucinations and facilitates debugging. |
| Outcome: | The proposed framework improves on three language-based reasoning tasks and improves accuracy and reasoning correctness. |
COUNT: COntrastive UNlikelihood Text Style Transfer for Text Detoxification (2023.findings-emnlp)
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Mohammad Mahdi Abdollah Pour, Parsa Farinneya, Manasa Bharadwaj, Nikhil Verma, Ali Pesaranghader, Scott Sanner
| Challenge: | Text detoxification is a task to ensure the generation of non-toxic and safe text. |
| Approach: | They propose a novel contrastive unlikelihood objective that combines rephrasing and identity mapping to effectively isolate and focus learning on non-toxic style transfer. |
| Outcome: | The proposed method achieves significant improvements in fluency, content preservation, and detoxification on two parallel datasets. |
Semantic XPath: Structured Agentic Memory Access for Conversational AI (2026.acl-demo)
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| Challenge: | Early ConvAI agents rely on an in-context approach that appends the growing conversation history to the model input, but this approach scales poorly under context-window limits. |
| Approach: | They propose a tree-structured memory module to access and update structured conversational memory. |
| Outcome: | The proposed system improves over flat-RAG baselines while using only 9.1% of the tokens required by in-context memory. |
Q-STRUM Debate: Query-Driven Contrastive Summarization for Recommendation Comparison (2025.findings-acl)
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| Challenge: | Existing contrastive summarization methods such as STRUM-LLM fail to clarify differences between items . emergence of large language models (LLMs) has revolutionized QCS capabilities . |
| Approach: | They propose a new method that generates focused and contrastive summaries by using debate-style prompting. |
| Outcome: | Experiments show that Q-STRUM Debate performs better than existing methods on key contrastive summarization criteria. |
DiffuDetox: A Mixed Diffusion Model for Text Detoxification (2023.findings-acl)
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Griffin Floto, Mohammad Mahdi Abdollah Pour, Parsa Farinneya, Zhenwei Tang, Ali Pesaranghader, Manasa Bharadwaj, Scott Sanner
| Challenge: | Existing text generation models that reduce toxicity of toxic text are inadequate for text detoxification tasks. |
| Approach: | They propose a conditional and unconditional diffusion model for text detoxification . conditional model takes toxic text as condition and reduces its toxicity . experimental results show the model achieves human-level fluency . |
| Outcome: | The proposed model reduces toxic text and produces diverse sentences . it can be used to train other models and ensure fluency . |
Bayesian Active Learning with Gaussian Processes Guided by LLM Relevance Scoring for Dense Passage Retrieval (2026.findings-acl)
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Junyoung Kim, Anton Korikov, Jiazhou Liang, Justin Cui, Yifan Simon Liu, Qianfeng Wen, Mark Zhao, Scott Sanner
| Challenge: | Existing approaches passively rely on first-stage dense retrievers, which leads to two limitations: failing to retrieve relevant passages in semantically distinct clusters and failing to propagate relevance signals to the broader corpus. |
| Approach: | They propose a framework that propagates sparse LLM relevance signals across the embedding space to guide global exploration. |
| Outcome: | Experiments show that the proposed framework outperforms existing approaches under the same budget on all four datasets. |
Open-World Planning via Lifted Regression with LLM-Inferred Affordances for Embodied Agents (2025.acl-long)
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Xiaotian Liu, Ali Pesaranghader, Hanze Li, Punyaphat Sukcharoenchaikul, Jaehong Kim, Tanmana Sadhu, Hyejeong Jeon, Scott Sanner
| Challenge: | Existing open-world planning methods rely on closed-world assumption (CWA) symbolic planners face combinatorial explosion of states and actions due to reliance on grounding. |
| Approach: | They propose an open-world planning approach integrating lifted regression with LLM-generated affordances. |
| Outcome: | The proposed approach outperforms state-of-the-art LLM planners and a grounded planner on three benchmarks. |
Evaluating Scene-based In-Situ Item Labeling for Immersive Conversational Recommendation (2026.findings-acl)
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| Challenge: | Existing methods for evaluating item labels fail to leverage scenario-specific information modalities, present redundant information that is visually inferable, and lack latent awareness of users' information needs. |
| Approach: | They propose a principled categorization of information needs into explicit intent satisfaction and proactive information needs and define evaluation metrics for item label selection. |
| Outcome: | The proposed evaluation framework is based on IR-, LLM-, and VLM-based methods across fashion, movie recommendation, and retail shopping scenarios. |
Right for Right Reasons: Large Language Models for Verifiable Commonsense Knowledge Graph Question Answering (2024.emnlp-main)
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| Challenge: | Existing Knowledge Graph Question Answering (KGQA) methods focus on answering factual questions, leaving questions involving commonsense reasoning unaddressed. |
| Approach: | They propose a commonsense KGQA methodology that axiomatically surfaces commonsensical knowledge of Large Language Models and grounding every factual reasoning step on KG triples. |
| Outcome: | The proposed method outperforms existing methods and reduces instances of hallucination and reasoning errors. |
Gaussian Process Optimization for Adaptable Multi-Objective Text Generation using Linearly-Weighted Language Models (2024.findings-naacl)
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| Challenge: | Multi-objective text generation requires a method to optimize for dynamic requirements of the overall objective. |
| Approach: | They propose a linear combination of objective-specific language models to efficiently adapt the decoding process and optimize for the desired overall objective without retraining one or more language models. |
| Outcome: | The proposed method outperforms other weighting schemes and standard baselines in a few iterations of decoding. |
Batched Self-Consistency Improves LLM Relevance Assessment and Ranking (2025.emnlp-main)
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| Challenge: | Existing work has focused on a one-by-one pointwise (PW) scoring strategy where each LLM call judges one passage at a time against the query. |
| Approach: | They propose to use batched PW methods to evaluate multiple passages per LLM call to improve efficiency and judgment quality by enabling content from multiple passage to be seen jointly. |
| Outcome: | The proposed methods improve efficiency and judgment quality by enabling content from multiple passages to be seen jointly. |
MA-DPR: Manifold-aware Distance Metrics for Dense Passage Retrieval (2025.emnlp-main)
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| Challenge: | Empirical evidence suggests that manifold-aware distance allows DPR to leverage context from related neighboring passages. |
| Approach: | They propose a "manifold-aware" distance metric that measures query–passage distance . they propose to model the intrinsic manifold structure of passages using a nearest-neighbor graph . |
| Outcome: | Empirical evidence suggests MA-DPR outperforms Euclidean and cosine distances on OOD . it can be applied to a wide range of dense embedding and retrieval tasks . |
Multimodal Item Scoring for Natural Language Recommendation via Gaussian Process Regression with LLM Relevance Judgments (2026.findings-acl)
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Yifan Simon Liu, Qianfeng Wen, Jiazhou Liang, Mark Zhao, Justin Cui, Anton Korikov, Armin Toroghi, Junyoung Kim, Scott Sanner
| Challenge: | Existing NLRec approaches use Dense Retrieval to compute item relevance scores . DR views the request as the sole relevance label, leading to a weak proxy for query relevance. |
| Approach: | They propose to use Gaussian Process Regression to model item relevance . they propose to combine LLM with a Gauss-based kernel to model multimodal relevance judging . |
| Outcome: | The proposed approach outperforms simpler unimodal kernels and baseline methods by up to 65% on four NLRec datasets and two LLM backbones. |