Blending Text and Symbols: A Path to Robust LLM Reasoning
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Large language models (LLMs) have demonstrated immense capabilities in natural language processing. They can generate remarkably human-like text, hold conversations, summarize long passages, and even attempt rudimentary reasoning.
However, despite their exceptional advances in semantic understanding of text, LLMs still face profound limitations when complex logical reasoning is required. Their comprehension remains surface-level, often missing deeper connections or failing at deductions requiring mathematical logic.
Two domains that expose these LLM reasoning deficiencies are tabular data and knowledge graphs. Tables containing structured statistics, relations, and properties abound in business analysis, science, and public policy contexts. Knowledge graphs assemble concepts, real-world entities, and their interrelations in intricate networks of facts modeled as graph nodes and edges.
Reasoning with such structured Data requires subtly balancing context with symbolic logic. For example, identifying statistical insights within tables benefits from understanding the semantics to contextualize what the numbers signify. Or solving analytical graph queries relies on manipulating logical graph patterns while tracking real-world entities.
Most LLMs today employ a purely textual reasoning strategy wherein the structured data gets represented in written natural language. Questions are posed in text requesting deductions or conclusions from the stated data. LLMs then scan and analyze the textual contents to produce inferred answers.
However, this approach strains when facing numerically intensive details in tables or logically complex traversals along knowledge graphs. The limited reasoning capacity of LLMs reaches exhaustion attempting to juggle vast arrays of numbers and multifaceted connections all in textual formats.
In contrast, symbolic reasoning employs structured logic languages including database query languages like SQL, graph traversal languages like SPARQL and Cypher, and general programming languages like Python and Java that feature optimized functions for numeric analysis.
By representing the analysis tasks symbolically rather than textually, the structured logic environment relieves the reasoning burden from LLMs. It also allows formulating rules, constraints, and computation sequences impossible to express purely textually. This expands the scope of what LLMs can achieve with external symbolic manipulation compared to just textual comprehension alone.
I. The Textual-Symbolic Divide
Fundamentally, there are two primary paradigms for enabling large language models to reason over structured data like tables and knowledge graphs:
a. Textual Reasoning
Textual reasoning involves directly prompting the LLMs with textual representations of the structured data, framed along with any questions in natural language text. For example, a table may be rendered in markdown format and a question asked about inferences drawn from the table, with the LLM expected to scan the textual table and question to deduce an answer.
This approach taps into the innate strength of language models – semantic understanding. By ingesting the structured data as text, semantic connections can be made to contextualize numbers and statistics or to pick up nuances in textual tables. Expert human prompts can further tune the textual representations to optimize legibility.
However, textual reasoning buckles when analysis requires multi-step logical chaining,Say if a question demands combining insights from one part of a large table with details from another segment to calculate statistics unseen directly. Or if it involves following a sequence of relationships across a knowledge graph to deduce new connections.
The limited reasoning capacity of LLMs has difficulty tracking such chains purely from textual descriptions without losing the thread. Numeric computations within large tables also challenge textual reasoning.
Ultimately, textual reasoning leverage LLMs' linguistic semantics but fails to support the rigorous logical and computational analysis required in many structured data use cases.
b. Symbolic Reasoning
Symbolic reasoning instead has LLMs interact with structured data indirectly – by generating code logic like SQL, SPARQL, Python that in turn interfaces with the tables or graphs.
For example, a Python code snippet that loads a table dataset and calculates new metrics using Pandas operations. Or a SPARQL query that traverses semantic relations in a knowledge graph to uncover hidden insights.
This coding logic lends itself well to tasks requiring aggregation, filtering, merging, and transformation. It also allows precise localization within huge datasets by referring to specific rows/cells. Mathematically intensive statistics and algorithmic analysis viable through symbolic environments further bolsters capabilities.
However, coding proficiency varies across different LLMs. Sophisticated logic beyond an LLM's competency hampers utility. Even simple syntax errors could break execution. Another challenge is correctly mapping natural language questions into appropriate symbolic code to yield the sought answers.
In the end, while symbolic reasoning expands the scope of possible analysis, the efficacy still depends on the LLMs proper formulation of executable symbolic logic.
c. The Divide
Textual reasoning empowers semantics while symbolic reasoning enables computational analysis. But neither in isolation can handle the full spectrum of reasoning vital for tables and knowledge graphs. This creates a divide where tasks fall in the gap between textual comprehension and pure symbolic manipulation.
Bridging this divide requires an integration that feeds textual insights to guide symbolic exploration while allowing symbolic findings to shape textual interpretation.
II. Complementary Powers United
llama-hub/llama_hub/llama_packs/tables/mix_self_consistency/mix_self_consistency.ipynb at main ·…
Rethinking Tabular Data Understanding with Large Language Models
The paper by Liu et al. (2023) delves into the challenges and potentials of tabular question answering (QA) through the lens of textual and symbolic reasoning. It establishes that neither textual reasoning alone, which leverages language models' semantic interpretation abilities, nor purely symbolic reasoning, which is limited by the coding proficiency and logical complexity models can handle, is adequate for the comprehensive tasks of tabular QA.
Textual reasoning, while intuitive for prompting large language models (LLMs), falls short in scenarios that require complex logical sequences or numerical calculations, such as pinpointing specific values in large tables. On the other hand, symbolic reasoning encounters difficulties with intricate logic or even simple syntax errors, which are beyond the competency of LLMs.
However, Liu et al. find that a combination of both approaches significantly enhances performance. This integrated method utilizes textual reasoning to provide context and inform the symbolic logic, which in turn deals with complex tasks such as counting and managing data relationships that text alone cannot handle. This synergistic relationship allows textual comprehension to guide symbolic exploration efficiently, while symbolic analysis fills in the gaps in textual understanding.
This hybrid approach emulates human reasoning, which combines logical deduction with contextual insights in a recursive manner. By blending textual and symbolic reasoning, the paper demonstrates improved cohesion and accuracy in tabular QA. The methodology, termed ‘mix self-consistency aggregation', achieves state-of-the-art performance on the WTQ dataset, underscoring the effectiveness of integrating textual and symbolic faculties in LLMs.
In exploring this integrated reasoning approach, the paper defines key elements of the problem space, including the representation of tables with rows and columns, various structural perturbations like transposing or shuffling rows, and the objective of evaluating a function to answer questions using structurally altered tables.
The paper compares two strategies: Direct Prompting (DP) for textual reasoning and Python Shell Agent (PyAgent) for symbolic reasoning.
Through rigorous quantitative analysis, the paper substantiates the superiority of the blended reasoning approach over either strategy used in isolation.
III. Adapting Tabular Best Practices to Knowledge Graphs
While research thus far has concentrated on fusing textual and symbolic strategies for tabular question answering, similar practices may prove beneficial for enhancing knowledge graph reasoning with large language models (LLMs).
Knowledge graphs encode rich relational information between entities through interconnected nodes and edges. Effectively harnessing insights from such structured data requires capabilities spanning both semantic comprehension and logical analysis.
Purely textual strategies struggle to represent the intricate connections within graphs. And exclusive symbolic manipulation misses high-level conceptual orientations. An integrated approach can synergize both facets.
Specifically, a blended textual-symbolic workflow could enable:
Textual Summarization
Prompt the LLM to explain the overall structure, scope and purpose of the knowledge graph through sentences. Summarizations confer a macro perspective on organizational philosophies and ontological choices made.
Along with summarizing the overall graph topology and semantics through text, vector similarity search techniques could embed entities, properties and relationships into semantic vector spaces. The LLM summarizes these vector spaces and provides examples of entity groupings based on vector similarities.
This allows pre-computing useful vector representations ahead of symbolic exploration. The vectors impart common background to prime LLM reasoning for more targeted symbolic traversals.
Symbolic Traversal
Generate logical symbolic queries tailored to traverse specific entity-relationship paths within the graph. This allows systematically mining new nodes and edges most relevant for targeted reasoning goals.
Popular languages include SPARQL for RDF graphs and Cypher for Property Graphs. Traversals can unravel hidden connectivity and surface non-explicit statistical insights through the graph topology.
Iterative Enhancement
Establish feedback loops between textual and symbolic findings to progressively enrich collective understanding of the graph's semantics. Surface novel textual annotations from symbolic traversals. And reformulate symbolic logic based on evolving textual comprehension.
With each iteration, insight specificity and contextual clarity improves. This ultimately constructs a robust mental framework for the knowledge graph.
Rather than purely sequential traversal, the LLM Compiler's structured concurrency management enables formulating and executing queries in parallel across disjoint graph segments. Independent regions can be mined simultaneously and then aggregated by the LLM.
Orchestrating Efficient Reasoning Over Knowledge Graphs with LLM Compiler Frameworks
The combinatorial space of composite multi-hop queries also grows rapidly. By handling dependencies and allowing safe parallel execution, potentially exponential gains in retrieval times are realizable at scale.
Further, incorporating vector similarities into the traversal logic can retrieve nodes based on proximity within learned embedding spaces. This acts as a semantic filter beyond syntactic pattern matching.
Additionally, using textual representations of graphical data as context when generating symbolic queries can direct more meaningful traversals. The text describes objectives while the symbols enact actions.
The research paper outlines an aggregation technique called Mix Self-Consistency that combines multiple textual and symbolic reasoning iterations, filtering only consistent responses via majority voting. An analogous approach tailored for multi-step knowledge graph reasoning tasks could prove beneficial.
Further research must investigate optimal strategies for interlacing textual and symbolic knowledge graph capabilities in LLMs. But the promise is evident.
Closing Thoughts
As large language models continue maturing from proficient linguistic systems into capable reasoning engines, innovating the architectural integration between textual and symbolic manipulative faculties becomes crucial.
Blending textual comprehension capacities with structured symbolic analysis brings us closer to realizing the vision of robust systems that interweave logic and language in a natural fashion.
The intricate tapestry of cognition within the human mind develops from a lifetime of assimilating logical, verbal and experiential modes into a versatile reasoning intellect. Present artificial systems still remain far from emulating the cohesion and fluidity manifest in biological general intelligence.
Yet the rapid progress on fronts like few-shot learning hints at the potential for significantly enhanced reasoning abilities with sustained research.
By creatively fusing capacities, we take steps towards more organically harmonizing the mathematical precision of symbolic manipulation with the contextual adaptability of semantic interpretation. Systems that transition seamlessly between precise chained deduction and high-level abstraction much like people demonstrate the flowering of versatile intelligence.
Architectural advancements that orchestrate large language models with a diversity of reasoning tools greatly further this vision. The LLM Compiler paradigm exemplifies techniques that structure access and interactions while retaining flexibility.
Innovations on the textual-symbolic frontier within large language model ecosystems underscore this exciting possibility for realizing integrative reasoning systems. With diligent research unraveling the intricate challenges behind effectively choreographing and conditioning these AI modalities, more cohesive hybrid reasoning awaits in the future evolution of artificial minds.
And while substantial obstacles persist, the sustained momentum towards harmonizing language, logic and learning carries profound implications for constructing versatile computational cognition that fluidly blends strengths to match multifaceted human intelligence on its own evolving turf.
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Chief AI Officer & Architect : Builder of Neuro-Symbolic AI Systems @Fribl enhanced GenAI for HR
