
Nitish Joshi
@nitishjoshi23
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PhD student at NYU | CS undergrad @IITBombay '20 | Research in Natural Language Processing (#NLProc). Birding @nitishbird
New York, USA
Joined June 2018
RT @michahu8: š¢ today's scaling laws often don't work for predicting downstream task performance. For some pretraining setups, smooth and pā¦.
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RT @ChengleiSi: Are AI scientists already better than human researchers?. We recruited 43 PhD students to spend 3 months executing researchā¦.
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RT @rico_angell: What causes jailbreaks to transfer between LLMs?. We find that jailbreak strength and model representation similarity predā¦.
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RT @jcyhc_ai: LLMs wonāt tell you how to make fake IDsābut will reveal the layouts/materials of IDs and make realistic photos if asked sepaā¦.
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RT @natolambert: Nice to see folks studying biases in RLHF / preference tuning all the way down to the datasets. I think many of the biasesā¦.
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RT @cmalaviya11: Ever wondered what makes language models generate overly verbose, vague, or sycophantic responses?. Our new paper investigā¦.
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RT @vishakh_pk: What does it mean for #LLM output to be novel?.In work w/ @jcyhc_ai, @JanePan_, @valeriechen_, @hhexiy we argue it needs tā¦.
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RT @YulinChen99: Reasoning models overthink, generating multiple answers during reasoning. Is it because they canāt tell which ones are rigā¦.
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RT @StringChaos: Excited to release R2E-Gym. - š„ 8.1K executable environments using synthetic data. - š§ Hybrid verifiers for enhanced infā¦.
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RT @yanda_chen_: My first paper @AnthropicAI is out!. We show that Chains-of-Thought often donāt reflect modelsā true reasoningāposing chalā¦.
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RT @nsaphra: 2018: Saliency maps give plausible interpretations of random weights, triggering skepticism and catalyzing the mechinterp cultā¦.
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RT @JanePan_: When benchmarks talk, do LLMs listen?. Our new paper shows that evaluating that code LLMs with interactive feedback significaā¦.
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RT @danish037: Remember this study about how LLM generated research ideas were rated to be more novel than expert-written ones? . We find aā¦.
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New work where we show that with the right training distribution, transformers can learn to search and internally implement an exponential path-merging algo. But they struggle to learn to search as the graph size increases, and simple solns like scaling doesn't resolve it.
šØšFoundational graph search task as testbed: for some distribution, transformers can learn to search (100% acc). We interpreted their algo!! But as graph size ā, transformers struggle. Scaling up # params does not help; CoT does not help. 1.5 years of learning in 10 pages!
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RT @omarsar0: Transformers Struggle to Learn to Search. Finds that transformer-based LLMs struggle to perform search robustly. Suggests tā¦.
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RT @vishakh_pk: Had a lot of fun poking holes at how LLMs capture diverse preferences with @chuanyang_jin, @hannahrosekirk and @hhexiy š§! Nā¦.
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RT @LauraRuis: How do LLMs learn to reason from data? Are they ~retrieving the answers from parametric knowledgeš¦? In our new preprint, weā¦.
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RT @lasha_nlp: āØIām on the faculty job market for 2024-2025! ⨠. My research focuses on advancing Responsible AIāenhancing factuality, robuā¦.
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RT @javirandor: Anyone may be able to compromise LLMs with malicious content posted online. With just a small amount of data, adversaries cā¦.
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