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Found 19 papers, 12 papers with code
Modifying Large Language Model Post-Training for Diverse Creative Writing
As creative writing tasks do not have singular correct answers, large language models (LLMs) trained to perform these tasks should be able to generate diverse valid outputs.
Whose Boat Does it Float? Improving Personalization in Preference Tuning via Inferred User Personas
However, this preference data format does not convey why users prefer responses that are chosen or rejected, so LLMs trained on these datasets cannot tailor responses to varied user needs.
Transformers Struggle to Learn to Search
This difficulty is not resolved even as the number of parameters is increased, suggesting that increasing model scale will not lead to robust search abilities.
Beyond the Binary: Capturing Diverse Preferences With Reward Regularization
Large language models (LLMs) are increasingly deployed via public-facing interfaces to interact with millions of users, each with diverse preferences.
Debate Helps Supervise Unreliable Experts
Comparing debate to a baseline we call consultancy, where a single expert argues for only one answer which is correct half of the time, we find that debate performs significantly better, with 84% judge accuracy compared to consultancy's 74%.
Creativity Support in the Age of Large Language Models: An Empirical Study Involving Emerging Writers
The development of large language models (LLMs) capable of following instructions and engaging in conversational interactions sparked increased interest in their utilization across various support tools.
Does Writing with Language Models Reduce Content Diversity?
We develop a set of diversity metrics and find that writing with InstructGPT (but not the GPT3) results in a statistically significant reduction in diversity.
Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD Examples
Given the intractably large size of the space of proofs, any model that is capable of general deductive reasoning must generalize to proofs of greater complexity.
Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs
Large language models (LLMs) have achieved widespread success on a variety of in-context few-shot tasks, but this success is typically evaluated via correctness rather than consistency.
Extrapolative Controlled Sequence Generation via Iterative Refinement
We study the problem of extrapolative controlled generation, i. e., generating sequences with attribute values beyond the range seen in training.
Reward Gaming in Conditional Text Generation
To align conditional text generation model outputs with desired behaviors, there has been an increasing focus on training the model using reinforcement learning (RL) with reward functions learned from human annotations.
Help me write a poem: Instruction Tuning as a Vehicle for Collaborative Poetry Writing
The core component of our system is a language model fine-tuned on a diverse collection of instructions for poetry writing.
Two-Turn Debate Doesn't Help Humans Answer Hard Reading Comprehension Questions
These findings suggest that, in the case of answering reading comprehension questions, debate is not a helpful format.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
Exploring the Role of Task Transferability in Large-Scale Multi-Task Learning
Recent work has found that multi-task training with a large number of diverse tasks can uniformly improve downstream performance on unseen target tasks.
QuALITY: Question Answering with Long Input Texts, Yes!
To enable building and testing models on long-document comprehension, we introduce QuALITY, a multiple-choice QA dataset with context passages in English that have an average length of about 5, 000 tokens, much longer than typical current models can process.
Machine-in-the-Loop Rewriting for Creative Image Captioning
Machine-in-the-loop writing aims to enable humans to collaborate with models to complete their writing tasks more effectively.
BBQ: A Hand-Built Bias Benchmark for Question Answering
It is well documented that NLP models learn social biases, but little work has been done on how these biases manifest in model outputs for applied tasks like question answering (QA).
Unsupervised Extractive Summarization using Pointwise Mutual Information
Unsupervised approaches to extractive summarization usually rely on a notion of sentence importance defined by the semantic similarity between a sentence and the document.
