Conversational AI | NLP | Headed by Dr. Dilek Hakkani-Tur and Dr. Gokhan Tur | UIUC | IllinoisCDS
ConvAI @ UIUC
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ToolRL: Reward is All Tool Learning Needs by Cheng Qian, @emrecanacikgoz.bsky.social, Qi He, Hongru Wang, Xiusi Chen, @dilekh.bsky.social, @gokhantur.bsky.social, Heng Ji
Read more here: arxiv.org/abs/2504.13958, x.com/emrecanacikg...
Thrilled to announce our new survey that explores the exciting possibilities and troubling risks of computational persuasion in the era of LLMs 🤖💬
📄Arxiv: arxiv.org/pdf/2505.07775
💻 GitHub: github.com/beyzabozdag/...
[5/5] Persuasion research is still playing catch-up, promising great advancements!✨
Thank you to my amazing co-authors! @shuhaib.bsky.social @xiaocheng-yang.bsky.social @HyeonjeongHa @ziruicheng.bsky.social @EsinDurmus @JiaxuanYou @HengJi @gokhantur.bsky.social @dilekh.bsky.social
ConvAI had a great NeurIPS season with four accepted papers to the main conference🎉 Find all the authors in San Diego this December ☀️
Neural Networks for Learnable and Scalable Influence Estimation of Instruction Fine-Tuning Data by @wonderingishika.bsky.social @dilekh.bsky.social
Read more here: x.com/wonderingish...
MIRAGE: A Benchmark for Multimodal Information-Seeking and Reasoning in Agricultural Expert-Guided Conversations by @vardhandongre.bsky.social Chi Gui, Hooshang Nayyeri, Shubham Garg, @gokhantur.bsky.social, @dilekh.bsky.social, Vikram Adve
Read more here: mirage-benchmark.github.io
Reinforcement Learning Finetunes Small Subnetworks in Large Language Models by @sagnikmukherjee.bsky.social, Lifan Yuan, @dilekh.bsky.social, Hao Peng
Read more here: arxiv.org/abs/2505.11711
x.com/saagnikkk/st...
While persuasive models are promising for social good, they can also be misused towards harmful behavior. Recent work by @beyzabozdag.bsky.social and @shuhaib.bsky.social aims to assess LLM persuasiveness and susceptibility towards persuasion.
🚀Our ICML 2025 paper introduces "Premise-Augmented Reasoning Chains" - a structured approach to induce explicit dependencies in reasoning chains.
By revealing the dependencies within chains, we significantly improve how LLM reasoning can be verified.
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📂 Code and data coming soon! Read our paper here: arxiv.org/abs/2502.02362
This would not have been possible without the contributions of @abhinav-chinta.bsky.social @takyoung.bsky.social Tarun and our amazing advisor @dilekh.bsky.social Special thanks to the members of @convai-uiuc.bsky.social
ConvAI @ UIUC
Reinforcement learning (RL) yields substantial improvements in large language models (LLMs) downstream task performance and alignment with human values. Surprisingly, such large gains result from upda...
Current Large Language Models (LLMs) often undergo supervised fine-tuning (SFT) to acquire tool use capabilities. However, SFT struggles to generalize to unfamiliar or complex tool use scenarios. Rece...
