洞見 - Language model analysis - # Scope ambiguity processing in large language models

Scope Ambiguities in Large Language Models: Insights into Semantic Structure and World Knowledge Representation

Q: How do language models represent and reason about scope relations in their internal representations?

Language models represent and reason about scope relations in their internal representations through the hierarchical structure of their neural networks. When processing sentences with scope ambiguities, the models encode the relationships between different semantic operators, such as quantifiers and negations, in their hidden layers. These relationships are learned during the training process, where the models adjust their weights to capture the patterns and dependencies in the input data. By doing so, the models can differentiate between different scope readings and make predictions based on the most likely interpretation. In terms of reasoning, language models use their learned representations to make inferences about the scope of different operators in a sentence. This involves analyzing the interactions between quantifiers, negations, adverbs, and other linguistic elements to determine the most probable interpretation. The models can then generate responses or predictions based on this analysis, demonstrating their ability to reason about scope relations in natural language.

Q: What other linguistic phenomena at the semantics-pragmatics interface could reveal insights about language model capabilities?

Several linguistic phenomena at the semantics-pragmatics interface could provide insights into language model capabilities beyond scope ambiguities. Some of these phenomena include: Anaphora Resolution: Language models' ability to correctly identify and interpret pronouns and their referents in a sentence can reveal their understanding of discourse coherence and the relationships between entities in a text. Presupposition and Entailment: Models' handling of presuppositions (assumptions made by speakers) and entailments (implications of a statement) can shed light on their ability to capture implicit meanings and context in language. Lexical Semantics: Understanding how models deal with polysemy (words with multiple meanings) and homonymy (words that are spelled the same but have different meanings) can provide insights into their lexical knowledge and semantic disambiguation capabilities. Modality and Mood: Examining how models interpret modal verbs (e.g., can, must) and mood markers (e.g., subjunctive mood) can reveal their understanding of certainty, possibility, and speaker attitudes in language. Speech Acts: Language models' performance in recognizing speech acts (e.g., requests, promises, apologies) can demonstrate their pragmatic understanding of language use and communicative intentions. By exploring these linguistic phenomena, researchers can gain a more comprehensive understanding of language model capabilities in various aspects of semantics and pragmatics.

Q: How might the ability to handle scope ambiguities relate to a language model's broader reasoning and inference capabilities?

The ability to handle scope ambiguities is closely linked to a language model's broader reasoning and inference capabilities. Successfully resolving scope ambiguities requires the model to consider multiple possible interpretations of a sentence and select the most contextually appropriate one. This process involves complex reasoning and inference skills, including: Semantic Understanding: Language models must understand the semantic relationships between different elements in a sentence to disambiguate scope ambiguities. This involves reasoning about quantifiers, negations, and other operators to determine the correct interpretation. Contextual Inference: Resolving scope ambiguities often requires considering the broader context of a sentence or discourse. Models need to infer implicit information, background knowledge, and discourse coherence to arrive at the correct interpretation. Logical Reasoning: Scope ambiguities are inherently logical in nature, involving the application of formal logic principles to determine the scope of operators. Models that can handle scope ambiguities effectively demonstrate strong logical reasoning abilities. Pragmatic Considerations: In some cases, resolving scope ambiguities may involve pragmatic considerations, such as speaker intentions, common knowledge, and conversational implicatures. Models that excel in this task showcase their pragmatic reasoning skills. Overall, the ability to handle scope ambiguities reflects a language model's capacity for nuanced reasoning, inference, and understanding of complex linguistic structures. By mastering scope ambiguities, models demonstrate a higher level of language comprehension and reasoning capabilities that extend to a wide range of tasks requiring sophisticated linguistic analysis.

核心概念

Large language models exhibit similar preferences to humans in interpreting scope ambiguous sentences and are sensitive to the presence of multiple readings in such sentences.

摘要

The paper investigates how different versions of autoregressive language models, including GPT-2, GPT-3/3.5, Llama 2, and GPT-4, handle scope ambiguous sentences and compares their performance to human judgments.

The key highlights and insights are:

Experiment 1 shows that more advanced language models like GPT-3.5, Llama 2 at 70B, and GPT-4 can exhibit similar scope reading preferences as humans, with a high level of accuracy. Smaller or less advanced models, however, struggle.
Experiment 2 suggests that a wide range of language models are sensitive to the meaning ambiguity in scope ambiguous sentences, as evidenced by positive mean α-scores and significant correlations between model α-scores and human proxy scores.
The results indicate that language models can capture different semantic structures corresponding to surface and inverse scope readings, and can also integrate background world knowledge when disambiguating scope ambiguous constructions.
Llama 2 chat models generally perform better than their vanilla counterparts, suggesting that fine-tuning on human feedback may improve a model's ability to handle scope ambiguities.
The expanded datasets used in the follow-up experiments confirm the generalizability of the findings.

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統計資料

Each farmer owns a donkey.
I didn't pass all of my exams.
I generally spar with two boxers.

引述

"Sentences containing multiple semantic op-
erators with overlapping scope often create
ambiguities in interpretation, known as scope
ambiguities."
"Scope disambiguation lies at the interface between
natural language semantics and background world
knowledge."

從以下內容提煉的關鍵洞見

Scope Ambiguities in Large Language Models

by Gaurav Kamat... 於 arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04332.pdf

Scope Ambiguities in Large Language Models

深入探究

How do language models represent and reason about scope relations in their internal representations?

Language models represent and reason about scope relations in their internal representations through the hierarchical structure of their neural networks. When processing sentences with scope ambiguities, the models encode the relationships between different semantic operators, such as quantifiers and negations, in their hidden layers. These relationships are learned during the training process, where the models adjust their weights to capture the patterns and dependencies in the input data. By doing so, the models can differentiate between different scope readings and make predictions based on the most likely interpretation.
In terms of reasoning, language models use their learned representations to make inferences about the scope of different operators in a sentence. This involves analyzing the interactions between quantifiers, negations, adverbs, and other linguistic elements to determine the most probable interpretation. The models can then generate responses or predictions based on this analysis, demonstrating their ability to reason about scope relations in natural language.

What other linguistic phenomena at the semantics-pragmatics interface could reveal insights about language model capabilities?

Several linguistic phenomena at the semantics-pragmatics interface could provide insights into language model capabilities beyond scope ambiguities. Some of these phenomena include:

Anaphora Resolution: Language models' ability to correctly identify and interpret pronouns and their referents in a sentence can reveal their understanding of discourse coherence and the relationships between entities in a text.

Presupposition and Entailment: Models' handling of presuppositions (assumptions made by speakers) and entailments (implications of a statement) can shed light on their ability to capture implicit meanings and context in language.

Lexical Semantics: Understanding how models deal with polysemy (words with multiple meanings) and homonymy (words that are spelled the same but have different meanings) can provide insights into their lexical knowledge and semantic disambiguation capabilities.

Modality and Mood: Examining how models interpret modal verbs (e.g., can, must) and mood markers (e.g., subjunctive mood) can reveal their understanding of certainty, possibility, and speaker attitudes in language.

Speech Acts: Language models' performance in recognizing speech acts (e.g., requests, promises, apologies) can demonstrate their pragmatic understanding of language use and communicative intentions.

By exploring these linguistic phenomena, researchers can gain a more comprehensive understanding of language model capabilities in various aspects of semantics and pragmatics.

How might the ability to handle scope ambiguities relate to a language model's broader reasoning and inference capabilities?

The ability to handle scope ambiguities is closely linked to a language model's broader reasoning and inference capabilities. Successfully resolving scope ambiguities requires the model to consider multiple possible interpretations of a sentence and select the most contextually appropriate one. This process involves complex reasoning and inference skills, including:

Semantic Understanding: Language models must understand the semantic relationships between different elements in a sentence to disambiguate scope ambiguities. This involves reasoning about quantifiers, negations, and other operators to determine the correct interpretation.

Contextual Inference: Resolving scope ambiguities often requires considering the broader context of a sentence or discourse. Models need to infer implicit information, background knowledge, and discourse coherence to arrive at the correct interpretation.

Logical Reasoning: Scope ambiguities are inherently logical in nature, involving the application of formal logic principles to determine the scope of operators. Models that can handle scope ambiguities effectively demonstrate strong logical reasoning abilities.

Pragmatic Considerations: In some cases, resolving scope ambiguities may involve pragmatic considerations, such as speaker intentions, common knowledge, and conversational implicatures. Models that excel in this task showcase their pragmatic reasoning skills.

Overall, the ability to handle scope ambiguities reflects a language model's capacity for nuanced reasoning, inference, and understanding of complex linguistic structures. By mastering scope ambiguities, models demonstrate a higher level of language comprehension and reasoning capabilities that extend to a wide range of tasks requiring sophisticated linguistic analysis.