65 citations · 125 across the 20 of their papers we have counts for
20 papers
FutureSim: Replaying World Events to Evaluate Adaptive Agents
Shashwat Goel, Nikhil Chandak, Arvindh Arun +5
AI agents are being increasingly deployed in dynamic, open-ended environments that require adapting to new information as it arrives. To efficiently measure this capability for rea…
Intrinsic Credit Assignment for Long Horizon Interaction
Ilze Amanda Auzina, Joschka Strüber, Sergio Hernández-Gutiérrez +3
How can we train agents to navigate uncertainty over long horizons? In this work, we propose ΔBelief-RL, which leverages a language model's own intrinsic beliefs to reward intermed…
Scaling Open-Ended Reasoning to Predict the Future
Nikhil Chandak, Shashwat Goel, Ameya Prabhu +2
High-stakes decision making involves reasoning under uncertainty about the future. In this work, we train language models to make predictions on open-ended forecasting questions. T…
Training AI Co-Scientists Using Rubric Rewards
Shashwat Goel, Rishi Hazra, Dulhan Jayalath +8
AI co-scientists are emerging as a tool to assist human researchers in achieving their research goals. A crucial feature of these AI co-scientists is the ability to generate a rese…
What if I ask in \textit{alia lingua}? Measuring Functional Similarity Across Languages
Debangan Mishra, Arihant Rastogi, Agyeya Negi +2
How similar are model outputs across languages? In this work, we study this question using a recently proposed model similarity metric applied to 20 languages and 47 subjects…
The Illusion of Diminishing Returns: Measuring Long Horizon Execution in LLMs
Akshit Sinha, Arvindh Arun, Shashwat Goel +2
Does continued scaling of large language models (LLMs) yield diminishing returns? In this work, we show that short-task benchmarks may give an illusion of slowing progress, as even…
Answer Matching Outperforms Multiple Choice for Language Model Evaluation
Nikhil Chandak, Shashwat Goel, Ameya Prabhu +2
Multiple choice benchmarks have long been the workhorse of language model evaluation because grading multiple choice is objective and easy to automate. However, we show multiple ch…
Pitfalls in Evaluating Language Model Forecasters
Daniel Paleka, Shashwat Goel, Jonas Geiping +1
Large language models (LLMs) have recently been applied to forecasting tasks, with some works claiming these systems match or exceed human performance. In this paper, we argue that…
Can Language Models Falsify? Evaluating Algorithmic Reasoning with Counterexample Creation
Shiven Sinha, Shashwat Goel, Ponnurangam Kumaraguru +3
There is growing excitement about the potential of Language Models (LMs) to accelerate scientific discovery. Falsifying hypotheses is key to scientific progress, as it allows claim…
Great Models Think Alike and this Undermines AI Oversight
Shashwat Goel, Joschka Struber, Ilze Amanda Auzina +6
As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these…
Enhancing AI Safety Through the Fusion of Low Rank Adapters
Satya Swaroop Gudipudi, Sreeram Vipparla, Harpreet Singh +2
Instruction fine-tuning of large language models (LLMs) is a powerful method for improving task-specific performance, but it can inadvertently lead to a phenomenon where models gen…
A Cognac Shot To Forget Bad Memories: Corrective Unlearning for Graph Neural Networks
Varshita Kolipaka, Akshit Sinha, Debangan Mishra +4
Graph Neural Networks (GNNs) are increasingly being used for a variety of ML applications on graph data. Because graph data does not follow the independently and identically distri…
The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning
Nathaniel Li, Alexander Pan, Anjali Gopal +54
The White House Executive Order on Artificial Intelligence highlights the risks of large language models (LLMs) empowering malicious actors in developing biological, cyber, and che…
Corrective Machine Unlearning
Shashwat Goel, Ameya Prabhu, Philip Torr +2
Machine Learning models increasingly face data integrity challenges due to the use of large-scale training datasets drawn from the Internet. We study what model developers can do i…
Representation Engineering: A Top-Down Approach to AI Transparency
Andy Zou, Long Phan, Sarah Chen +18
In this paper, we identify and characterize the emerging area of representation engineering (RepE), an approach to enhancing the transparency of AI systems that draws on insights f…
Proportional Aggregation of Preferences for Sequential Decision Making
Nikhil Chandak, Shashwat Goel, Dominik Peters
We study the problem of fair sequential decision making given voter preferences. In each round, a decision rule must choose a decision from a set of alternatives where each voter r…
Low impact agency: review and discussion
Danilo Naiff, Shashwat Goel
Powerful artificial intelligence poses an existential threat if the AI decides to drastically change the world in pursuit of its goals. The hope of low-impact artificial intelligen…
Towards Adversarial Evaluations for Inexact Machine Unlearning
Shashwat Goel, Ameya Prabhu, Amartya Sanyal +3
Machine Learning models face increased concerns regarding the storage of personal user data and adverse impacts of corrupted data like backdoors or systematic bias. Machine Unlearn…
From Pivots to Graphs: Augmented CycleDensity as a Generalization to One Time InverseConsultation
Shashwat Goel, Kunwar Shaanjeet Singh Grover
This paper describes an approach used to generate new translations using raw bilingual dictionaries as part of the 4th Task Inference Across Dictionaries (TIAD 2021) shared task. W…
A Search for Technosignatures Around 31 Sun-like Stars with the Green Bank Telescope at 1.15-1.73 GHz
Jean-Luc Margot, Pavlo Pinchuk, Robert Geil +37
We conducted a search for technosignatures in April of 2018 and 2019 with the L-band receiver (1.15-1.73 GHz) of the 100 m diameter Green Bank Telescope. These observations focused…