🚨New Paper!🚨 How do reasoning LLMs handle inferences that have no deterministic answer? We find that they diverge from humans in some significant ways, and fail to reflect human uncertainty… 🧵(1/10)

Our new paper in #PNAS (bit.ly/4fcWfma) presents a surprising finding—when words change meaning, older speakers rapidly adopt the new usage; inter-generational differences are often minor. w/ Michelle Yang, ‪@sivareddyg.bsky.social‬ , @msonderegger.bsky.social‬ and @dallascard.bsky.social‬👇(1/12)

A blizzard is raging through Montreal when your friend says “Looks like Florida out there!” Humans easily interpret irony, while LLMs struggle with it. We propose a 𝘳𝘩𝘦𝘵𝘰𝘳𝘪𝘤𝘢𝘭-𝘴𝘵𝘳𝘢𝘵𝘦𝘨𝘺-𝘢𝘸𝘢𝘳𝘦 probabilistic framework as a solution. Paper: arxiv.org/abs/2506.09301 to appear @ #ACL2025 (Main)

"Build the web for agents, not agents for the web" This position paper argues that rather than forcing web agents to adapt to UIs designed for humans, we should develop a new interface optimized for web agents, which we call Agentic Web Interface (AWI). arxiv.org/abs/2506.10953

Excited to share the results of my recent internship! We ask 🤔 What subtle shortcuts are VideoLLMs taking on spatio-temporal questions? And how can we instead curate shortcut-robust examples at a large-scale? We release: MVPBench Details 👇🔬

Do LLMs hallucinate randomly? Not quite. Our #ACL2025 (Main) paper shows that hallucinations under irrelevant contexts follow a systematic failure mode — revealing how LLMs generalize using abstract classes + context cues, albeit unreliably. 📎 Paper: arxiv.org/abs/2505.22630 1/n

Congratulations to Mila members @adadtur.bsky.social , Gaurav Kamath and @sivareddyg.bsky.social for their SAC award at NAACL! Check out Ada's talk in Session I: Oral/Poster 6. Paper: arxiv.org/abs/2502.05670

Exciting release! AgentRewardBench offers that much-needed closer look at evaluating agent capabilities: automatic vs. human eval. Important findings here, especially on the popular LLM judges. Amazing work by @xhluca.bsky.social & team!

Xing Han Lu@xhluca.bsky.social · last yr.

AgentRewardBench: Evaluating Automatic Evaluations of Web Agent Trajectories We are releasing the first benchmark to evaluate how well automatic evaluators, such as LLM judges, can evaluate web agent trajectories.

AgentRewardBench: Evaluating Automatic Evaluations of Web Agent Trajectories We are releasing the first benchmark to evaluate how well automatic evaluators, such as LLM judges, can evaluate web agent trajectories.

And thoughtology is now on Arxiv! Read more about R1 reasoning 🐋💭 across visual, cultural and psycholinguistic tasks at the link below: 🔗 arxiv.org/abs/2504.07128

DeepSeek-R1 Thoughtology: Let's <think> about LLM Reasoning

Large Reasoning Models like DeepSeek-R1 mark a fundamental shift in how LLMs approach complex problems. Instead of directly producing an answer for a given input, DeepSeek-R1 creates detailed multi-st...

arxiv.org

Sara Vera Marjanovic@saravera.bsky.social · last yr.

Models like DeepSeek-R1 🐋 mark a fundamental shift in how LLMs approach complex problems. In our preprint on R1 Thoughtology, we study R1’s reasoning chains across a variety of tasks; investigating its capabilities, limitations, and behaviour. 🔗: mcgill-nlp.github.io/thoughtology/

DeepSeek-R1 Thoughtology: Let’s <think> about LLM reasoning 142-page report diving into the reasoning chains of R1. It spans 9 unique axes: safety, world modeling, faithfulness, long context, etc. Now on arxiv: arxiv.org/abs/2504.07128

Introducing the DeepSeek-R1 Thoughtology -- the most comprehensive study of R1 reasoning chains/thoughts ✨. Probably everything you need to know about R1 thoughts. If we missed something, please let us know.

Sara Vera Marjanovic@saravera.bsky.social · last yr.

Models like DeepSeek-R1 🐋 mark a fundamental shift in how LLMs approach complex problems. In our preprint on R1 Thoughtology, we study R1’s reasoning chains across a variety of tasks; investigating its capabilities, limitations, and behaviour. 🔗: mcgill-nlp.github.io/thoughtology/

Models like DeepSeek-R1 🐋 mark a fundamental shift in how LLMs approach complex problems. In our preprint on R1 Thoughtology, we study R1’s reasoning chains across a variety of tasks; investigating its capabilities, limitations, and behaviour. 🔗: mcgill-nlp.github.io/thoughtology/

Check out our new workshop on Actionable Interpretability @ ICML 2025. We are also looking forward to submissions that take a position on the future of interpretability research more broadly. 👇

Mor Geva@megamor2.bsky.social · last yr.

🎉 Our Actionable Interpretability workshop has been accepted to #ICML2025! 🎉 > Follow @actinterp.bsky.social > Website actionable-interpretability.github.io @talhaklay.bsky.social @anja.re @mariusmosbach.bsky.social @sarah-nlp.bsky.social @iftenney.bsky.social Paper submission deadline: May 9th!

📢Excited to announce our upcoming workshop - Vision Language Models For All: Building Geo-Diverse and Culturally Aware Vision-Language Models (VLMs-4-All) @CVPR 2025! 🌐 sites.google.com/view/vlms4all

Instruction-following retrievers can efficiently and accurately search for harmful and sensitive information on the internet! 🌐💣 Retrievers need to be aligned too! 🚨🚨🚨 Work done with the wonderful Nick and @sivareddyg.bsky.social 🔗 mcgill-nlp.github.io/malicious-ir/ Thread: 🧵👇

Web agents powered by LLMs can solve complex tasks, but our analysis shows that they can also be easily misused to automate harmful tasks. See the thread below for more details on our new web agent safety benchmark: SafeArena and Agent Risk Assessment framework (ARIA).

Xing Han Lu@xhluca.bsky.social · last yr.

Agents like OpenAI Operator can solve complex computer tasks, but what happens when users use them to cause harm, e.g. spread misinformation? To find out, we introduce SafeArena (safearena.github.io), a benchmark to assess the capabilities of web agents to complete harmful web tasks. A thread 👇

The potential for malicious misuse of LLM agents is a serious threat. That's why we created SafeArena, a safety benchmark for web agents. See the thread and our paper for details: arxiv.org/abs/2503.04957 👇

SafeArena: Evaluating the Safety of Autonomous Web Agents

LLM-based agents are becoming increasingly proficient at solving web-based tasks. With this capability comes a greater risk of misuse for malicious purposes, such as posting misinformation in an onlin...

arxiv.org

Xing Han Lu@xhluca.bsky.social · last yr.

Agents like OpenAI Operator can solve complex computer tasks, but what happens when users use them to cause harm, e.g. spread misinformation? To find out, we introduce SafeArena (safearena.github.io), a benchmark to assess the capabilities of web agents to complete harmful web tasks. A thread 👇

Llamas browsing the web look cute, but they are capable of causing a lot of harm! Check out our new Web Agents ∩ Safety benchmark: SafeArena! Paper: arxiv.org/abs/2503.04957

Xing Han Lu@xhluca.bsky.social · last yr.

Agents like OpenAI Operator can solve complex computer tasks, but what happens when users use them to cause harm, e.g. spread misinformation? To find out, we introduce SafeArena (safearena.github.io), a benchmark to assess the capabilities of web agents to complete harmful web tasks. A thread 👇

Agents like OpenAI Operator can solve complex computer tasks, but what happens when users use them to cause harm, e.g. spread misinformation? To find out, we introduce SafeArena (safearena.github.io), a benchmark to assess the capabilities of web agents to complete harmful web tasks. A thread 👇

📢New Paper Alert!🚀 Human alignment balances social expectations, economic incentives, and legal frameworks. What if LLM alignment worked the same way?🤔 Our latest work explores how social, economic, and contractual alignment can address incomplete contracts in LLM alignment🧵

Check out the new MMTEB benchmark🙌 if you are looking for an extensive, reproducible and open-source evaluation of text embedders!

Kenneth Enevoldsen@kennethenevoldsen.eurosky.social · last yr.

I am delighted to announce that we have released 🎊 MMTEB 🎊, a large-scale collaboration working on efficient multilingual evaluation of embedding models. This work implements >500 evaluation tasks across >1000 languages and covers a wide range of use cases and domains🩺👩‍💻⚖️

I'm fortunate to have collaborated with a team of brilliant researchers on this colossal project 🎊 Among the tasks i contributed, i'm most excited about the contextual web element retrieval task derived from weblinx, which i think is a crucial component for building web agents!

Kenneth Enevoldsen@kennethenevoldsen.eurosky.social · last yr.

I am delighted to announce that we have released 🎊 MMTEB 🎊, a large-scale collaboration working on efficient multilingual evaluation of embedding models. This work implements >500 evaluation tasks across >1000 languages and covers a wide range of use cases and domains🩺👩‍💻⚖️

Presenting ✨ 𝐂𝐇𝐀𝐒𝐄: 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐢𝐧𝐠 𝐬𝐲𝐧𝐭𝐡𝐞𝐭𝐢𝐜 𝐝𝐚𝐭𝐚 𝐟𝐨𝐫 𝐞𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 ✨ Work w/ fantastic advisors Dima Bahdanau and @sivareddyg.bsky.social Thread 🧵:

I am delighted to announce that we have released 🎊 MMTEB 🎊, a large-scale collaboration working on efficient multilingual evaluation of embedding models. This work implements >500 evaluation tasks across >1000 languages and covers a wide range of use cases and domains🩺👩‍💻⚖️

Interested in knowing more about LLMs agents and in contributing to this topic?🚀 📢We're thrilled to announce REALM: The first Workshop for Research on Agent Language Models 🤖 #ACL2025NLP in Vienna 🎻 We have an exciting lineup of speakers 🗓️ Submit your work by *March 1st* @aclmeeting.bsky.social

Glad to see BM25S (bm25s.github.io) has been downloaded 1M times on PyPi 🎉 Numbers aside, it makes me happy to hear the positive experience from friends working on retrieval. It's good to know that people near me are enjoying it! Discussion: github.com/xhluca/bm25s/discussions

Retrieval seems to be a rather challenging problem even in the era of LLMs: a lot of benchmarks do not seem to be saturated yet, e.g. the best score on a 7-year old benchmark like Dbpedia is around 0.53 NDCG@10. I wonder if it's a lack of focus or if they are truly challenging problems to solve...

I'll get straight to the point. We trained 2 new models. Like BERT, but modern. ModernBERT. Not some hypey GenAI thing, but a proper workhorse model, for retrieval, classification, etc. Real practical stuff. It's much faster, more accurate, longer context, and more useful. 🧵

Really glad that this work is out! Agentlab and browsergym will be, in my opinion, very important components of web agent research and will play an important role in the toolkit of most web agent researchers. Read the paper if you are interested in learning more about what the platform covers!

Alexandre Lacoste@alex-lacoste.bsky.social · 2y ago

We’re really excited to release this large collaborative work for unifying web agent benchmarks under the same roof. In this TMLR paper, we dive in-depth into #BrowserGym and #AgentLab. We also present some unexpected performances from Claude 3.5-Sonnet