Papers with interactive systems
People will agree what I think: Investigating LLM’s False Consensus Effect (2025.findings-naacl)
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| Challenge: | Recent studies have focused on the False Consensus Effect (FCE) where individuals overestimate the extent to which others share their beliefs or behaviors. |
| Approach: | They conduct two studies to examine the FCE phenomenon in Large Language Models (LLMs) they find that popular LLMs have FCE and that they have different prompting styles. |
| Outcome: | The proposed model is popular among LLM users and specifies the conditions when FCE becomes more or less prevalent compared to normal usage. |
Incremental Processing in the Age of Non-Incremental Encoders: An Empirical Assessment of Bidirectional Models for Incremental NLU (2020.emnlp-main)
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| Challenge: | a number of languages are processed incrementally, but the best ones do not . we test five models on various datasets and compare their performance using three incremental evaluation metrics. |
| Approach: | They investigate how bidirectional LSTMs and Transformers behave under incremental interfaces . they propose to use bidirectional encoders in incremental mode while retaining non-incremental quality . |
| Outcome: | The proposed models perform better under incremental interfaces than the "omni-directional" BERT model, which achieves better non-incremental performance, but is impacted more by the incremental access. |
Machine-in-the-Loop Rewriting for Creative Image Captioning (2022.naacl-main)
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| Challenge: | Prior work has shown that providing users with a machine-written draft or sentence-level continuations has limited success since the generated text tends to deviate from users’ intention. |
| Approach: | They propose to train a rewriting model that modifies specified spans of text within the user’s original draft to introduce descriptive and figurative elements in the text. |
| Outcome: | The proposed model is rated more helpful by users than a baseline infilling language model on a user study through Amazon Mechanical Turk. |
Towards Incremental Transformers: An Empirical Analysis of Transformer Models for Incremental NLU (2021.emnlp-main)
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| Challenge: | Recent work attempts to apply incremental processing to NLUs but this is computationally expensive and does not scale efficiently for long sequences. |
| Approach: | They propose to apply Transformers incrementally via restart-incrementality by repeatedly feeding, to an unchanged model, increasingly longer input prefixes to produce partial outputs. |
| Outcome: | The proposed model has better incremental performance and faster inference speed compared to the standard Transformer and LT with restart-incrementality, at the cost of part of the non-incremental quality. |
Towards Proactive Personalization through Profile Customization for Individual Users in Dialogues (2026.findings-acl)
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| Challenge: | Existing alignment methods focus on universal human values or static, single-turn preferences, thereby failing to address the critical needs of long-term personalization and the initial user cold-start problem. |
| Approach: | They propose a user-centric lifelong agent that continuously infers and adapts to user preferences. |
| Outcome: | The proposed agent achieves superior performance over strong prompt-based and policy optimization baselines, not only in idealized but also in noisy conversational contexts. |
Measuring Immediate Adaptation Performance for Neural Machine Translation (N19-1)
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| Challenge: | Incremental domain adaptation improves interactive machine translation performance . users of interactive systems are sensitive to the speed of adaptation . |
| Approach: | They propose to measure the speed of lexical acquisition for in-domain vocabulary . they propose to use this to choose the most suitable adaptation method for neural machine translation . |
| Outcome: | The proposed measures measure the speed of lexical acquisition for in-domain vocabulary . they show that the most suitable adaptation method is chosen from a range of different techniques . |
Frugal Prompting for Dialog Models (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) are used in natural language processing tasks with an unrealistic speed and effectiveness. |
| Approach: | They propose more compact ways of providing dialog history information while ensuring good performance and reducing model’s inference-API costs. |
| Outcome: | The proposed models have the optimal usable-information density while maintaining good performance and reducing model’s inference-API costs. |
Combining Cognitive Modeling and Reinforcement Learning for Clarification in Dialogue (2020.coling-main)
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| Challenge: | In many domains, dialogue systems need to work collaboratively with users to reconstruct meaning . this requires a system that can give targeted, effective feedback about the system’s understanding . |
| Approach: | They propose a system that collaborates on reference tasks that distinguish arbitrarily varying color patches from similar distractors and use crowd workers to test their approach. |
| Outcome: | The proposed system can distinguish varying color patches from distractors and elicit correct answers that the system understands. |
Uncertainty Quantification in LLM Agents: Foundations, Emerging Challenges, and Opportunities (2026.acl-long)
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Changdae Oh, Seongheon Park, To Eun Kim, Jiatong Li, Wendi Li, Samuel Yeh, Sean Du, Hamed Hassani, Paul Bogdan, Dawn Song, Sharon Li
| Challenge: | Uncertainty quantification (UQ) for large language models is a key building block for daily applications. |
| Approach: | They propose a general formulation of agent UQ that subsumes broad classes of existing UQ setups. |
| Outcome: | The proposed framework is based on the first general formulation of agent UQ that subsumes broad classes of existing setups. |
DIESEL: A Lightweight Inference-Time Safety Enhancement for Language Models (2025.findings-acl)
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| Challenge: | Large language models generate outputs that are not aligned with human values, such as toxic content, malicious use cases, and vulnerabilities to adversarial jailbreak attacks. |
| Approach: | They propose a lightweight inference-guidance technique that can be seamlessly integrated into any autoregressive LLM to semantically filter undesirable content during generation. |
| Outcome: | The proposed technique can be integrated into any autoregressive LLM to semantically filter undesirable content during generation. |
Agentic-ToM: Cognition-Inspired Agentic Processing For Enhancing Theory of Mind Reasoning (2025.findings-emnlp)
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| Challenge: | Current models struggle with reasoning about others’ perspectives, limiting their ability to attribute mental states to oneself and others. |
| Approach: | They propose to embed psychologically-grounded functions into LLMs to enable them to attribute mental states to oneself and others, known as Theory of Mind. |
| Outcome: | The proposed approach outperforms baselines on three ToM datasets without task-specific modifications. |
HiAgent: Hierarchical Working Memory Management for Solving Long-Horizon Agent Tasks with Large Language Model (2025.acl-long)
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| Challenge: | Existing approaches to optimize agent performance by incorporating entire historical action-observation pairs into LLMs are redundant in long-horizon tasks. |
| Approach: | They propose a framework that leverages subgoals as memory chunks to manage working memory of LLM-based agents hierarchically. |
| Outcome: | The proposed framework achieves a twofold increase in success rate and reduces the average number of steps required by 3.8. |
Effects of Collaboration on the Performance of Interactive Theme Discovery Systems (2026.acl-long)
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Alvin Po-Chun Chen, Rohan Das, Dananjay Srinivas, Alexandra Barry, Maksim Seniw, Maria Leonor Pacheco
| Challenge: | NLP-assisted systems to support qualitative data analysis have gained considerable traction, but no unified evaluation framework exists to account for the many different settings in which qualitative researchers may employ them. |
| Approach: | They propose a framework to evaluate the way collaboration settings may produce different research outcomes across a variety of interactive systems. |
| Outcome: | The proposed framework evaluates the impact of synchronous vs. asynchronous collaboration on consistency, cohesiveness, and correctness of qualitative research outcomes. |