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LLM Parsing (Beta)

LLM Parsing (Beta)

Overview🔗︎

Textkernel offers a resume parsing option that seamlessly integrates the power of Large Language Models (LLMs) with Textkernel technology. The result is a parser that is better than can be built with each technology individually.

The LLM Parser can be enabled on a per-request basis. An add-on cost applies to each transaction that has the LLM Parser enabled.

Limitations and Caveats🔗︎

  • The LLM Parser is currently only available for English resumes.
  • All fields mentioned in the low-usage fields document are not output. Additionally, the following fields are not output:

    • Individual education raw text
    • Normalized local and international degree codes/descriptions
    • Individual position descriptions
    • Responses can be several seconds slower when using the LLM Parser.
    • The LLM Parser can generate information that is factually incorrect or misleading. It may also output plausible-sounding but false information. However, the LLM Parser offers improved accuracy compared to the standard parser, helping to offset any potential hallucinations.
    • In exceptional cases, the LLM Parser returns an error or takes more than 60 seconds to respond. In such cases, we automatically fall back to our standard parser. The add-on credits for using the LLM parser will still be applied.

Frequently Asked Questions🔗︎

Check our LLM Parser FAQ

FlexRequests (Beta)🔗︎

Powered by our LLM Parsing Engine, all Resume Parsing requests can include FlexRequests to answer any question about a resume. FlexRequests are available with both the standard, non-LLM parsing engine, and the LLM parsing engine. Each FlexRequest needs to include a Prompt, a DataType, and an Identifier. The possible DataTypes can be found here.

FlexRequests can be set on a per-request basis. An add-on cost applies to each transaction that uses FlexRequests.

Example FlexRequests🔗︎

"FlexRequests": [
            {
                "Prompt": "This person has experience working internationally.",
                "Identifier": "InternationalWork",
                "DataType": "bool"
            },
            {
                "Prompt": "Based on this person's most recent job description, infer the two digit NAICS code for the industry this person is currently working in.",
                "Identifier": "Industry",
                "DataType": "numeric"
            },
            {
                "Prompt": "How many years has this person spent in a leadership position?",
                "Identifier": "YearsLeadership",
                "DataType": "numeric"
            },
            {
                "Prompt": "What online courses or trainings has this person completed?",
                "Identifier": "OnlineCourses",
                "DataType": "list"
            },
            {
                "Prompt": "Is this person a better fit for a senior or junior level position?",
                "Identifier": "PositionLevel",
                "DataType": "enumeration",
                "EnumerationValues": ["Senior", "Junior"]
            }
        ]

Example Replies🔗︎

"FlexResponse": {
            ...,
            "FlexRequests": [
                    {
                        "Identifier": "InternationalWork",
                        "Reply": "true"
                    },
                    {
                        "Identifier": "Industry",
                        "Reply": "54"
                    },
                    {
                        "Identifier": "YearsLeadership",
                        "Reply": "10"
                    },
                    {
                        "Identifier": "OnlineCourses",
                        "ReplyList": [
                            "Sun Secure Global Desktop (Tarantella) System Administration"
                        ]
                    },
                    {
                        "Identifier": "PositionLevel",
                        "Reply": "Senior"
                    }
                ]
            }

Limitations and Caveats🔗︎

  • FlexRequests are currently only available for English resumes.
  • Responses can be several seconds slower when using FlexRequests.
  • FlexRequests are powered by Large Language Models (LLMs). LLMs can generate information that is factually incorrect or misleading. It may also output plausible-sounding but false information.