1 citations · 1 across the 9 of their papers we have counts for
12 papers
Language Models are Lazy Poets: Creative Mode Collapse at Scale
Jacob A. Matthews, Sil Hamilton
While aligned large language models (LLMs) can successfully complete many creative tasks, their outputs tend to be highly self-similar. Though solutions to this problem of "mode co…
InvestigationWorlds: An Agentic Environment for Legal Investigation
Albert Yu Sun, Andrew Benard, Sil Hamilton +8
We introduce InvestigationWorlds, an agentic environment for legal investigation. We build on an underused artifact of U.S. civil litigation: the summary judgment motion. This moti…
CorporateBench: Large-Scale Q&A Benchmarking with Temporal Knowledge Bases
Sil Hamilton, Albert Yu Sun, Oscar J. Romero +4
LLMs are increasingly able to answer complex questions about enterprise-scale document collections. But evaluation is hard: companies don't want to share internal communications, a…
The Plot Thins: Uniformity and Linearity in Literary Summaries
Rebecca M. M. Hicke, Sil Hamilton, David Mimno +1
Works of literature are complicated; they balance plot, suspense, surprise, and artistic expression. Summaries of literature prioritize plot, and therefore may deviate from their s…
Elias in the Lighthouse, Again? Diagnosing Low Diversity in LLM Stories
Sil Hamilton, David Mimno
LLM-generated stories are a popular use case, but they show very low variability. We sample 20,000 total stories from four current models using five prompts. We find that 11 words…
Attention Flows: Tracing LLM Conceptual Engagement via Story Summaries
Rebecca M. M. Hicke, Sil Hamilton, David Mimno +1
Although LLM context lengths have grown, there is evidence that their ability to integrate information across long-form texts has not kept pace. We evaluate one such understanding…
NarraBench: A Comprehensive Framework for Narrative Benchmarking
Sil Hamilton, Matthew Wilkens, Andrew Piper
We present NarraBench, a theory-informed taxonomy of narrative-understanding tasks, as well as an associated survey of 78 existing benchmarks in the area. We find significant need…
Too Long, Didn't Model: Decomposing LLM Long-Context Understanding With Novels
Sil Hamilton, Rebecca M. M. Hicke, Matthew Wilkens +1
Although the context length of large language models (LLMs) has increased to millions of tokens, evaluating their effectiveness beyond needle-in-a-haystack approaches has proven di…
The Zero Body Problem: Probing LLM Use of Sensory Language
Rebecca M. M. Hicke, Sil Hamilton, David Mimno
Sensory language expresses embodied experiences ranging from taste and sound to excitement and stomachache. This language is of interest to scholars from a wide range of domains in…
A City of Millions: Mapping Literary Social Networks At Scale
Sil Hamilton, Rebecca M. M. Hicke, David Mimno +1
We release 70,509 high-quality social networks extracted from multilingual fiction and nonfiction narratives. We additionally provide metadata for 30,000 of these texts (73\%…
Lost in Space: Finding the Right Tokens for Structured Output
Sil Hamilton, David Mimno
General-purpose language models are trained to produce varied natural language outputs, but for some tasks, like annotation or classification, we need more specific output formats.…
Detecting Mode Collapse in Language Models via Narration
Sil Hamilton
No two authors write alike. Personal flourishes invoked in written narratives, from lexicon to rhetorical devices, imply a particular author--what literary theorists label the impl…