Papers by Gokhan Tur
Prior Beliefs Prejudice LLM-as-Judge: Evidence from Persuasion Evaluation (2026.findings-acl)
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| Challenge: | Large Language Models are increasingly used as judges to evaluate text quality, content and assess arguments. |
| Approach: | They propose to exploit belief-conditioned rating inflation by using persuasion-based probing to examine persuasive arguments. |
| Outcome: | The proposed model fails to evaluate persuasive arguments based on belief alignment . the model fails in three of the three tasks, with belief-conditioned rating inflation accounting for 88% of cases. |
Controllable Text Generation with Focused Variation (2020.findings-emnlp)
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Lei Shu, Alexandros Papangelis, Yi-Chia Wang, Gokhan Tur, Hu Xu, Zhaleh Feizollahi, Bing Liu, Piero Molino
| Challenge: | Focused-Variation Network (FVN) is a new model to control language generation. |
| Approach: | They propose a model that learns discrete latent spaces for each attribute inside codebooks and uses them to generate fluent text. |
| Outcome: | The proposed model can generate fluent and mostly coherent text on two text generation datasets with annotated content and style, and show state-of-the-art performance as assessed by automatic and human evaluations. |
Can a Single Model Master Both Multi-turn Conversations and Tool Use? CoALM: A Unified Conversational Agentic Language Model (2025.acl-long)
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Emre Can Acikgoz, Jeremiah Greer, Akul Datta, Ze Yang, William Zeng, Oussama Elachqar, Emmanouil Koukoumidis, Dilek Hakkani-Tür, Gokhan Tur
| Challenge: | Large Language Models (LLMs) with API-calling capabilities enabled building effective Language Agents (LA) current approaches excel in one domain but underperform in the other. |
| Approach: | They propose a unified approach that integrates both conversational and agentic capabilities. |
| Outcome: | The proposed model outperforms top domain-specific models across three benchmarks. |
SMART: Self-Aware Agent for Tool Overuse Mitigation (2025.findings-acl)
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Cheng Qian, Emre Can Acikgoz, Hongru Wang, Xiusi Chen, Avirup Sil, Dilek Hakkani-Tür, Gokhan Tur, Heng Ji
| Challenge: | Current Large Language Models (LLMs) lack self-awareness to balance reasoning and tool use, increasing computational overhead. |
| Approach: | They propose a paradigm that enhances an agent’s self-awareness to optimize task handling and reduce tool overuse. |
| Outcome: | The proposed model reduces tool use by 24% while improving performance by over 37%. |
TT-SI: Self-Improving LLM Agents with Test-Time Training (2026.findings-acl)
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| Challenge: | Existing methods for language model fine-tuning are expensive and inefficient . existing methods rarely assess whether a training sample provides novel information . |
| Approach: | They propose a test-time self-improvement algorithm that generates a sample that model struggles with . they also explore Test-Time Distillation, which leverages 'stronger supervisors' |
| Outcome: | The proposed algorithm improves performance with +5.48% absolute accuracy gain on average across benchmarks. |
Current Agents Fail to Leverage World Model as Tool for Foresight (2026.acl-long)
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Cheng Qian, Emre Can Acikgoz, Bingxuan Li, Xiusi Chen, Yuji Zhang, Bingxiang He, Qinyu Luo, Gokhan Tur, Dilek Hakkani-Tür, Yunzhu Li, Heng Ji
| Challenge: | Generative world models could be used to enhance agents' cognition . agents are expected to operate in settings where tasks unfold over long horizons and involve intricate chains of interdependent decisions. |
| Approach: | They propose to use vision-language models as external simulators to enhance cognition . they find that agents rarely invoke simulation and misuse predicted rollouts . |
| Outcome: | The proposed model could be used to predict future states rather than short-horizon reasoning . the model could also be used for real-world planning and robotics . |
CGF: Constrained Generation Framework for Query Rewriting in Conversational AI (2022.emnlp-industry)
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Jie Hao, Yang Liu, Xing Fan, Saurabh Gupta, Saleh Soltan, Rakesh Chada, Pradeep Natarajan, Chenlei Guo, Gokhan Tur
| Challenge: | Large-scale conversational AI agents such as Alexa, Siri and Google Assistant help millions of users to perform a lot of tasks. |
| Approach: | They propose a Constrained Generation Framework for query rewriting at global and personalized levels. |
| Outcome: | The proposed framework significantly boosts the query rewriting performance. |
MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages (2023.acl-long)
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Jack FitzGerald, Christopher Hench, Charith Peris, Scott Mackie, Kay Rottmann, Ana Sanchez, Aaron Nash, Liam Urbach, Vishesh Kakarala, Richa Singh, Swetha Ranganath, Laurie Crist, Misha Britan, Wouter Leeuwis, Gokhan Tur, Prem Natarajan
| Challenge: | We present the MASSIVE dataset–Multilingual Amazon Slu resource package (SLURP) for Slot-filling, Intent classification, and Virtual assistant evaluation. |
| Approach: | They present a 1M-example dataset of Amazon Slu utterances . they localize the dataset into 50 typologically diverse languages . |
| Outcome: | The proposed model includes exact match accuracy, intent classification accuracy, and slot-filling F1 score. |
Know Your Mistakes: Towards Preventing Overreliance on Task-Oriented Conversational AI Through Accountability Modeling (2025.acl-long)
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| Challenge: | Recent LLMs are known to hallucinate, producing responses that seem plausible but are factually incorrect. |
| Approach: | They propose an accountability model for LLM-based task-oriented dialogue agents to address user overreliance via friction turns in cases of model uncertainty and errors associated with dialogue state tracking (DST). |
| Outcome: | The proposed model improves joint goal accuracy (JGA) of DST output by 3% on two established benchmarks. |
VISITRON: Visual Semantics-Aligned Interactively Trained Object-Navigator (2022.findings-acl)
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Ayush Shrivastava, Karthik Gopalakrishnan, Yang Liu, Robinson Piramuthu, Gokhan Tur, Devi Parikh, Dilek Hakkani-Tur
| Challenge: | Interactive robots navigating photo-realistic environments need to be trained to handle dynamic nature of dialogue and vision-and-language navigation (VLN). |
| Approach: | They propose a Transformer-based multi-modal navigator that is better suited to the interactive regime inherent to Cooperative Vision-and-Dialog Navigation (CVDN). |
| Outcome: | The proposed model is trained to identify and associate object-level concepts and semantics between the environment and dialogue history and identify when to interact vs. navigate via imitation learning of a binary classification head. |