Lost in Space: Optimizing Tokens for Grammar-Constrained Decoding

• URL: http://arxiv.org/abs/2502.14969v1
• Date: Thu, 20 Feb 2025 19:06:18 GMT
• Title: Lost in Space: Optimizing Tokens for Grammar-Constrained Decoding
• Authors: Sil Hamilton, David Mimno,
• Abstract summary: We ask whether there are systematic differences between grammars that appear semantically similar to humans.<n>We test four popular model families with five token formats on four NLP benchmarks.<n>All models perform most accurately when instructed to classify with real numbers.
• Score: 3.5757761767474876
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. LLM systems increasingly support structured output, sampling tokens according to a grammar, which enforces a format but which can also reduce performance. We ask whether there are systematic differences between grammars that appear semantically similar to humans. To answer this question, we test four popular model families with five token formats on four NLP benchmarks. All models perform most accurately when instructed to classify with real numbers. Performance also improves by 5%-10% when models are instructed to return tokens incorporating leading whitespace, which we find can help models avoid structural deficiencies in subword token representations. Format-based differences are largest for smaller models that are often used for local laptop-scale inference. We present best practices for researchers using language models as zero-shot classifiers with structured output.

Related papers

• From Language Models over Tokens to Language Models over Characters [54.123846188068384]
  Modern language models are internally -- and mathematically -- distributions over token strings rather than emphcharacter strings.<n>This paper presents algorithms for converting token-level language models to character-level ones.
  arXiv  Detail & Related papers  (2024-12-04T21:19:20Z)
• Language Models for Text Classification: Is In-Context Learning Enough? [54.869097980761595]
  Recent foundational language models have shown state-of-the-art performance in many NLP tasks in zero- and few-shot settings. An advantage of these models over more standard approaches is the ability to understand instructions written in natural language (prompts) This makes them suitable for addressing text classification problems for domains with limited amounts of annotated instances.
  arXiv  Detail & Related papers  (2024-03-26T12:47:39Z)
• Learning Mutually Informed Representations for Characters and Subwords [26.189422354038978]
  We introduce the entanglement model, aiming to combine character and subword language models. Inspired by vision-language models, our model treats characters and subwords as separate modalities. We evaluate our model on text classification, named entity recognition, POS-tagging, and character-level sequence labeling.
  arXiv  Detail & Related papers  (2023-11-14T02:09:10Z)
• BenchCLAMP: A Benchmark for Evaluating Language Models on Syntactic and Semantic Parsing [55.058258437125524]
  We introduce BenchCLAMP, a Benchmark to evaluate Constrained LAnguage Model Parsing. We benchmark eight language models, including two GPT-3 variants available only through an API. Our experiments show that encoder-decoder pretrained language models can achieve similar performance or surpass state-of-the-art methods for syntactic and semantic parsing when the model output is constrained to be valid.
  arXiv  Detail & Related papers  (2022-06-21T18:34:11Z)
• Interpreting Language Models with Contrastive Explanations [99.7035899290924]
  Language models must consider various features to predict a token, such as its part of speech, number, tense, or semantics. Existing explanation methods conflate evidence for all these features into a single explanation, which is less interpretable for human understanding. We show that contrastive explanations are quantifiably better than non-contrastive explanations in verifying major grammatical phenomena.
  arXiv  Detail & Related papers  (2022-02-21T18:32:24Z)
• On The Ingredients of an Effective Zero-shot Semantic Parser [95.01623036661468]
  We analyze zero-shot learning by paraphrasing training examples of canonical utterances and programs from a grammar. We propose bridging these gaps using improved grammars, stronger paraphrasers, and efficient learning methods. Our model achieves strong performance on two semantic parsing benchmarks (Scholar, Geo) with zero labeled data.
  arXiv  Detail & Related papers  (2021-10-15T21:41:16Z)
• Models In a Spelling Bee: Language Models Implicitly Learn the Character Composition of Tokens [22.55706811131828]
  We probe the embedding layer of pretrained language models. We show that models learn the internal character composition of whole word and subword tokens.
  arXiv  Detail & Related papers  (2021-08-25T11:48:05Z)
• Constrained Language Models Yield Few-Shot Semantic Parsers [73.50960967598654]
  We explore the use of large pretrained language models as few-shot semantics. The goal in semantic parsing is to generate a structured meaning representation given a natural language input. We use language models to paraphrase inputs into a controlled sublanguage resembling English that can be automatically mapped to a target meaning representation.
  arXiv  Detail & Related papers  (2021-04-18T08:13:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.