Archiki Prasad ✈️ ICML
@ArchikiPrasad
PhD student @uncnlp in #NLProc, #ML, advised by @mohitban47 | @Apple Scholar in AI/ML | Intern @GoogleDeepMind; Prev (intern): @AIatMeta (FAIR), @allenai_org
🥳🥳 Honored and grateful to be awarded the 2025 @Apple Scholars in AI/ML PhD Fellowship! ✨ Huge shoutout to my advisor @mohitban47 for his guidance, and many thanks to my lab mates at @uncnlp, past collaborators, internship advisors, and mentors for their support 😊🙏…
🥳 Gap year update: I'll be joining @allen_ai/@UW for 1 year (Sep2025-Jul2026 -> @JHUCompSci) & looking forward to working with amazing folks there, incl. @RanjayKrishna, @HannaHajishirzi, Ali Farhadi. 🚨 I’ll also be recruiting PhD students for my group at @JHUCompSci for Fall…
Sharing some personal updates 🥳: - I've completed my PhD at @unccs! 🎓 - Starting Fall 2026, I'll be joining the Computer Science dept. at Johns Hopkins University (@JHUCompSci) as an Assistant Professor 💙 - Currently exploring options + finalizing the plan for my gap year (Aug…
🎉 Our paper, GenerationPrograms, which proposes a modular framework for attributable text generation, has been accepted to @COLM_conf! GenerationPrograms produces a program that executes to text, providing an auditable trace of how the text was generated and major gains on…
Excited to share GenerationPrograms! 🚀 How do we get LLMs to cite their sources? GenerationPrograms is attributable by design, producing a program that executes text w/ a trace of how the text was generated! Gains of up to +39 Attribution F1 and eliminates uncited sentences,…
I’ll be at #ICML2025 this week to present ScPO: 📌 Wednesday, July 16th, 11:00 AM-1:30 PM 📍East Exhibition Hall A-B, E-2404 Stop by or reach out to chat about improving reasoning in LLMs, self-training, or just tips about being on the job market next cycle! 😃
🚨 Self-Consistency Preference Optimization (ScPO)🚨 - New self-training method without human labels - learn to make the model more consistent! - Works well for reasoning tasks where RMs fail to evaluate correctness. - Close to performance of supervised methods *without* labels,…
🎉 Glad to see our work on handling conflicting & noisy evidence and ambiguous queries in RAG systems (via a new benchmark & multi-agent debate method) has been accepted to #COLM2025 @COLM_conf!! 🇨🇦 Congrats to Han on leading this effort. More details in the thread below and…
🚨Real-world retrieval is messy: queries can be ambiguous, or documents may conflict/have incorrect/irrelevant info. How can we jointly address all these problems? We introduce: ➡️ RAMDocs, a challenging dataset with ambiguity, misinformation, and noise. ➡️ MADAM-RAG, a…
🥳 Excited to share our work -- Retrieval-Augmented Generation with Conflicting Evidence -- on addressing conflict in RAG due to ambiguity, misinformation, and noisy/irrelevant evidence has been accepted to @COLM_conf #COLM2025! Our new benchmark RAMDocs proves challenging for…
🚨Real-world retrieval is messy: queries can be ambiguous, or documents may conflict/have incorrect/irrelevant info. How can we jointly address all these problems? We introduce: ➡️ RAMDocs, a challenging dataset with ambiguity, misinformation, and noise. ➡️ MADAM-RAG, a…
🚨Introducing Video-RTS: Resource-Efficient RL for Video Reasoning with Adaptive Video TTS! While RL-based video reasoning with LLMs has advanced, the reliance on large-scale SFT with extensive video data and long CoT annotations remains a major bottleneck. Video-RTS tackles…
🎉 Very excited to see TaCQ — our work on task-conditioned mixed-precision quantization that draws on interpretability methods — accepted to @COLM_conf #COLM2025 with strong scores and a nice shoutout from the AC! Kudos to Hanqi on leading this effort!
🚨Announcing TaCQ 🚨 a new mixed-precision quantization method that identifies critical weights to preserve. We integrate key ideas from circuit discovery, model editing, and input attribution to improve low-bit quant., w/ 96% 16-bit acc. at 3.1 avg bits (~6x compression)…
🥳Our work UTGen & UTDebug on teaching LLMs to generate effective unit tests & improve code debugging/generation has been accepted to @COLM_conf #COLM2025! Stay tuned for more exciting results -- e.g., using 32B-scale UTGen models to improve debugging with frontier models like…
🚨 Excited to share: "Learning to Generate Unit Tests for Automated Debugging" 🚨 which introduces ✨UTGen and UTDebug✨ for teaching LLMs to generate unit tests (UTs) and debugging code from generated tests. UTGen+UTDebug improve LLM-based code debugging by addressing 3 key…
🥳Excited to share that I’ll be joining @unccs as postdoc this fall. Looking forward to work with @mohitban47 & amazing students at @unc_ai_group. I'll continue working on retrieval, aligning knowledge modules with LLM's parametric knowledge, and expanding to various modalities.
🎉 Yay, welcome @hyunji_amy_lee -- super excited to have you join us as a postdoc! 🤗 Welcome to our MURGe-Lab + @unc_ai_group + @unccs family & the beautiful Research Triangle area -- looking forward to the many fun+impactful collaborations together 🔥
🥳Excited to share that I’ll be joining @unccs as postdoc this fall. Looking forward to work with @mohitban47 & amazing students at @unc_ai_group. I'll continue working on retrieval, aligning knowledge modules with LLM's parametric knowledge, and expanding to various modalities.
Excited to share GenerationPrograms! 🚀 How do we get LLMs to cite their sources? GenerationPrograms is attributable by design, producing a program that executes text w/ a trace of how the text was generated! Gains of up to +39 Attribution F1 and eliminates uncited sentences,…
🎉Excited to share that I’ll be starting my CS PhD journey at @UNC @unccs this fall! 🎓 I’ll be working with the renowned @mohitban47 at @uncnlp — a dream comes true! ✨ Huge thanks to everyone who's helped me get here. Can't wait to begin this new life and research journey! 🧳🚀
Big news! 🎉 I’m joining UNC-Chapel Hill as an Assistant Professor in Computer Science starting next year! Before that, I’ll be spending time @OpenAI working on LLM privacy. @unccs @uncnlp
🚨 Introducing LAQuer, accepted to #ACL2025 (main conf)! LAQuer provides more granular attribution for LLM generations: users can just highlight any output fact (top), and get attribution for that input snippet (bottom). This reduces the amount of text the user has to read by 2…
RAG systems struggle to simultaneously handle ambiguous queries, conflicting information, and noise. This paper introduces the RAMDocs dataset for these complex scenarios and proposes MADAM-RAG. MADAM-RAG uses multiple LLM agents debating evidence from documents; an aggregator…
Interesting work! Also provides additional evidence that our ScPO (Self-Consistency Preference Optimization) direction (Maj vote-based rewards, see fig below) works quite well -- without any labels. @ArchikiPrasad
🤯 We cracked RLVR with... Random Rewards?! Training Qwen2.5-Math-7B with our Spurious Rewards improved MATH-500 by: - Random rewards: +21% - Incorrect rewards: +25% - (FYI) Ground-truth rewards: + 28.8% How could this even work⁉️ Here's why: 🧵 Blogpost: tinyurl.com/spurious-rewar…
Welcome to the @uncnlp @unccs family and the beautiful Chapel Hill/RTP area, Joy! 🤗 Looking forward to exciting research and good times together in your PhD 💙
I’m thrilled to share that I’ll be joining the University of North Carolina at Chapel Hill for my CS PhD this fall!! 🎓💙 @UNC I’ll be working with the amazing @mohitban47 at @uncnlp. Grateful to everyone who’s supported me, excited for this new chapter! 🚀
Thrilled to share that I’ll be joining the College of Computing and Data Science at Nanyang Technological University (NTU) (@NTUsg) as an Assistant Professor, starting in August 2025 🇸🇬🥳 I’ll continue my research on building trustworthy and continually adaptable multimodal AI,…
Sharing some personal updates 🥳: - I've completed my PhD at @unccs! 🎓 - Starting Fall 2026, I'll be joining the Computer Science dept. at Johns Hopkins University (@JHUCompSci) as an Assistant Professor 💙 - Currently exploring options + finalizing the plan for my gap year (Aug…
🚨Announcing RAM 2 workshop @ COLM25 - call for papers🚨 - 10 years on, we present the sequel to the classic RAM🐏 (Reasoning, Attention, Memory) workshop that took place in 2015 at the cusp of major change in the area. Now in 2025 we reflect on what's happened and discuss the…