Paper-Conference

Improving Conversational Recommendation with Contextual Adaptation of External Recommenders and LLM-Based Reranking
A conversational recommendation framework that combines external recommenders with LLM reranking.
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Improving Conversational Recommendation with Contextual Adaptation of External Recommenders and LLM-Based Reranking
HistoricRAG: Evidence-Centered Newspaper Retrieval for Misinformation-Resilient Question Answering
An EDBT 2026 demo for evidence-centered historical newspaper retrieval and question answering.
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HistoricRAG: Evidence-Centered Newspaper Retrieval for Misinformation-Resilient Question Answering
Conversing with the Past: Immersive Access to Cultural Digital Archives with LLMs and Spatial Interfaces
An IUI Companion 2026 paper on LLM-powered spatial access to cultural archives.
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Conversing with the Past: Immersive Access to Cultural Digital Archives with LLMs and Spatial Interfaces
MuSLR: Multimodal Symbolic Logical Reasoning
A NeurIPS 2025 benchmark and method for multimodal symbolic logical reasoning.
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MuSLR: Multimodal Symbolic Logical Reasoning
SkyLadder: Better and Faster Pretraining via Context Window Scheduling
A NeurIPS 2025 method for faster and stronger pretraining through context window scheduling.
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SkyLadder: Better and Faster Pretraining via Context Window Scheduling
Discursive Circuits: How Do Language Models Understand Discourse Relations?
We introduce a new task, Completion under Discourse Relation (CuDR), to reveal the underlying transformer circuits responsible for discourse understanding.
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Discursive Circuits: How Do Language Models Understand Discourse Relations?
Beyond In-Context Learning: Aligning Long-form Generation of Large Language Models via Task-Inherent Attribute Guidelines
In-context learning (ICL) is an important yet not fully understood ability of pre-trained large language models (LLMs). It can greatly …
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What Makes a Good Natural Language Prompt?
As large language models (LLMs) have progressed towards more human-like and human–AI communications prevalent, prompting has emerged as …
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LLMs Are Biased Towards Output Formats! Systematically Evaluating and Mitigating Output Format Bias of LLMs
We present the first systematic evaluation examining format bias in performance of large language models (LLMs). Our approach …
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Aligning Large Language Models with Human Opinions through Persona Selection and Value-Belief-Norm Reasoning
Reasoning and predicting human opinions with large language models (LLMs) is essential yet challenging. Current methods employ …
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