Zobrazeno 1 - 10
of 11 218
pro vyhledávání: '"Kim, Yoon A."'
Humans are not homo economicus (i.e., rational economic beings). As humans, we exhibit systematic behavioral biases such as loss aversion, anchoring, framing, etc., which lead us to make suboptimal economic decisions. Insofar as such biases may be em
Externí odkaz:
http://arxiv.org/abs/2408.02784
Autor:
Guo, Han, Brandon, William, Cholakov, Radostin, Ragan-Kelley, Jonathan, Xing, Eric P., Kim, Yoon
The deployment of large language models (LLMs) is often constrained by memory bandwidth, where the primary bottleneck is the cost of transferring model parameters from the GPU's global memory to its registers. When coupled with custom kernels that fu
Externí odkaz:
http://arxiv.org/abs/2407.10960
When asked to summarize articles or answer questions given a passage, large language models (LLMs) can hallucinate details and respond with unsubstantiated answers that are inaccurate with respect to the input context. This paper describes a simple a
Externí odkaz:
http://arxiv.org/abs/2407.07071
The same real-life questions posed to different individuals may lead to different answers based on their unique situations. For instance, whether a student is eligible for a scholarship depends on eligibility conditions, such as major or degree requi
Externí odkaz:
http://arxiv.org/abs/2406.11784
Transformers with linear attention (i.e., linear transformers) and state-space models have recently been suggested as a viable linear-time alternative to transformers with softmax attention. However, these models still underperform transformers espec
Externí odkaz:
http://arxiv.org/abs/2406.06484
Aligning Large Language Models (LLMs) to cater to different human preferences, learning new skills, and unlearning harmful behavior is an important problem. Search-based methods, such as Best-of-N or Monte-Carlo Tree Search, are performant, but impra
Externí odkaz:
http://arxiv.org/abs/2405.06639
Aligning language models (LMs) based on human-annotated preference data is a crucial step in obtaining practical and performant LM-based systems. However, multilingual human preference data are difficult to obtain at scale, making it challenging to e
Externí odkaz:
http://arxiv.org/abs/2404.12318
This paper studies the relationship between the surface form of a mathematical problem and its solvability by large language models. We find that subtle alterations in the surface form can significantly impact the answer distribution and the solve ra
Externí odkaz:
http://arxiv.org/abs/2404.11500
Autor:
Nrusimha, Aniruddha, Mishra, Mayank, Wang, Naigang, Alistarh, Dan, Panda, Rameswar, Kim, Yoon
We consider the problem of accurate quantization for language models, where both the weights and activations are uniformly quantized to 4 bits per parameter, the lowest bitwidth format natively supported by GPU hardware. In this context, the key chal
Externí odkaz:
http://arxiv.org/abs/2404.03605
We describe an approach for aligning an LLM-based dialogue agent based on global (i.e., dialogue-level) rewards, while also taking into account naturally-occurring multimodal signals. At a high level, our approach (dubbed GELI) learns a local, turn-l
Externí odkaz:
http://arxiv.org/abs/2403.11330