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# Causal program-aided language (CPAL) chain ## Motivation This builds on the recent [PAL](https://arxiv.org/abs/2211.10435) to stop LLM hallucination. The problem with the [PAL](https://arxiv.org/abs/2211.10435) approach is that it hallucinates on a math problem with a nested chain of dependence. The innovation here is that this new CPAL approach includes causal structure to fix hallucination. For example, using the below word problem, PAL answers with 5, and CPAL answers with 13. "Tim buys the same number of pets as Cindy and Boris." "Cindy buys the same number of pets as Bill plus Bob." "Boris buys the same number of pets as Ben plus Beth." "Bill buys the same number of pets as Obama." "Bob buys the same number of pets as Obama." "Ben buys the same number of pets as Obama." "Beth buys the same number of pets as Obama." "If Obama buys one pet, how many pets total does everyone buy?" The CPAL chain represents the causal structure of the above narrative as a causal graph or DAG, which it can also plot, as shown below.  . The two major sections below are: 1. Technical overview 2. Future application Also see [this jupyter notebook](https://github.com/borisdev/langchain/blob/master/docs/extras/modules/chains/additional/cpal.ipynb) doc. ## 1. Technical overview ### CPAL versus PAL Like [PAL](https://arxiv.org/abs/2211.10435), CPAL intends to reduce large language model (LLM) hallucination. The CPAL chain is different from the PAL chain for a couple of reasons. * CPAL adds a causal structure (or DAG) to link entity actions (or math expressions). * The CPAL math expressions are modeling a chain of cause and effect relations, which can be intervened upon, whereas for the PAL chain math expressions are projected math identities. PAL's generated python code is wrong. It hallucinates when complexity increases. ```python def solution(): """Tim buys the same number of pets as Cindy and Boris.Cindy buys the same number of pets as Bill plus Bob.Boris buys the same number of pets as Ben plus Beth.Bill buys the same number of pets as Obama.Bob buys the same number of pets as Obama.Ben buys the same number of pets as Obama.Beth buys the same number of pets as Obama.If Obama buys one pet, how many pets total does everyone buy?""" obama_pets = 1 tim_pets = obama_pets cindy_pets = obama_pets + obama_pets boris_pets = obama_pets + obama_pets total_pets = tim_pets + cindy_pets + boris_pets result = total_pets return result # math result is 5 ``` CPAL's generated python code is correct. ```python story outcome data name code value depends_on 0 obama pass 1.0 [] 1 bill bill.value = obama.value 1.0 [obama] 2 bob bob.value = obama.value 1.0 [obama] 3 ben ben.value = obama.value 1.0 [obama] 4 beth beth.value = obama.value 1.0 [obama] 5 cindy cindy.value = bill.value + bob.value 2.0 [bill, bob] 6 boris boris.value = ben.value + beth.value 2.0 [ben, beth] 7 tim tim.value = cindy.value + boris.value 4.0 [cindy, boris] query data { "question": "how many pets total does everyone buy?", "expression": "SELECT SUM(value) FROM df", "llm_error_msg": "" } # query result is 13 ``` Based on the comments below, CPAL's intended location in the library is `experimental/chains/cpal` and PAL's location is`chains/pal`. ### CPAL vs Graph QA Both the CPAL chain and the Graph QA chain extract entity-action-entity relations into a DAG. The CPAL chain is different from the Graph QA chain for a few reasons. * Graph QA does not connect entities to math expressions * Graph QA does not associate actions in a sequence of dependence. * Graph QA does not decompose the narrative into these three parts: 1. Story plot or causal model 4. Hypothetical question 5. Hypothetical condition ### Evaluation Preliminary evaluation on simple math word problems shows that this CPAL chain generates less hallucination than the PAL chain on answering questions about a causal narrative. Two examples are in [this jupyter notebook](https://github.com/borisdev/langchain/blob/master/docs/extras/modules/chains/additional/cpal.ipynb) doc. ## 2. Future application ### "Describe as Narrative, Test as Code" The thesis here is that the Describe as Narrative, Test as Code approach allows you to represent a causal mental model both as code and as a narrative, giving you the best of both worlds. #### Why describe a causal mental mode as a narrative? The narrative form is quick. At a consensus building meeting, people use narratives to persuade others of their causal mental model, aka. plan. You can share, version control and index a narrative. #### Why test a causal mental model as a code? Code is testable, complex narratives are not. Though fast, narratives are problematic as their complexity increases. The problem is LLMs and humans are prone to hallucination when predicting the outcomes of a narrative. The cost of building a consensus around the validity of a narrative outcome grows as its narrative complexity increases. Code does not require tribal knowledge or social power to validate. Code is composable, complex narratives are not. The answer of one CPAL chain can be the hypothetical conditions of another CPAL Chain. For stochastic simulations, a composable plan can be integrated with the [DoWhy library](https://github.com/py-why/dowhy). Lastly, for the futuristic folk, a composable plan as code allows ordinary community folk to design a plan that can be integrated with a blockchain for funding. An explanation of a dependency planning application is [here.](https://github.com/borisdev/cpal-llm-chain-demo) --- Twitter handle: @boris_dev --------- Co-authored-by: Boris Dev <borisdev@Boriss-MacBook-Air.local> |
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🦜️🔗 LangChain
⚡ Building applications with LLMs through composability ⚡
Looking for the JS/TS version? Check out LangChain.js.
Production Support: As you move your LangChains into production, we'd love to offer more comprehensive support. Please fill out this form and we'll set up a dedicated support Slack channel.
Quick Install
pip install langchain
or
conda install langchain -c conda-forge
🤔 What is this?
Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. However, using these LLMs in isolation is often insufficient for creating a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge.
This library aims to assist in the development of those types of applications. Common examples of these applications include:
❓ Question Answering over specific documents
- Documentation
- End-to-end Example: Question Answering over Notion Database
💬 Chatbots
- Documentation
- End-to-end Example: Chat-LangChain
🤖 Agents
- Documentation
- End-to-end Example: GPT+WolframAlpha
📖 Documentation
Please see here for full documentation on:
- Getting started (installation, setting up the environment, simple examples)
- How-To examples (demos, integrations, helper functions)
- Reference (full API docs)
- Resources (high-level explanation of core concepts)
🚀 What can this help with?
There are six main areas that LangChain is designed to help with. These are, in increasing order of complexity:
📃 LLMs and Prompts:
This includes prompt management, prompt optimization, a generic interface for all LLMs, and common utilities for working with LLMs.
🔗 Chains:
Chains go beyond a single LLM call and involve sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.
📚 Data Augmented Generation:
Data Augmented Generation involves specific types of chains that first interact with an external data source to fetch data for use in the generation step. Examples include summarization of long pieces of text and question/answering over specific data sources.
🤖 Agents:
Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end-to-end agents.
🧠 Memory:
Memory refers to persisting state between calls of a chain/agent. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of chains/agents that use memory.
🧐 Evaluation:
[BETA] Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.
For more information on these concepts, please see our full documentation.
💁 Contributing
As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
For detailed information on how to contribute, see here.